Research| Volume 103, ISSUE 8, P6950-6966, August 01, 2020

# Consumer desires and perceptions of lactose-free milk

Open ArchivePublished:June 03, 2020

## ABSTRACT

Fluid milk consumption has declined in the United States, but lactose-free dairy milk (LFM) sales have steadily increased. It is important to understand how consumers perceive LFM and what consumers value when purchasing LFM. This study characterized consumer perceptions and preferences for LFM. Three 1.5-h focus groups (n = 25), an online survey (n = 331), trained panel descriptive analysis, and a consumer acceptance taste test (n = 160) were conducted with LFM consumers. Focus groups were evaluated by frequency of responses. From the focus group findings, we found that price was a primary choice driver of LFM. Habit and flavor familiarity with cow milk were a major driver of selection of LFM over plant-based alternatives for consumers. Higher sweetness and lower viscosity were the primary sensory differences between LFM and traditional milk, and were viewed negatively in general. The online survey included Kano questions, maximum difference scaling, and an adaptive choice-based conjoint. The data gathered from these techniques provided insight into the perceptions and purchase habits of consumers. Kano data demonstrated consumer attitudes toward the presence or absence of product attributes. The maximum difference scaling scaled the importance of product attributes to consumers. An adaptive choice-based conjoint provided insight into consumer purchase habits by simulating a purchase decision through an online interface. The attributes evaluated included price, packaging material, package size, lactose removal method, shelf life, sweetness, texture, nutrition claims, and label claims. Survey responses were analyzed by univariate and multivariate statistics. Survey results quantitatively confirmed many focus group observations. Price, texture, sweetness, shelf life, and package size were the most important attributes to LFM consumers. A low price, ultrapasteurized LFM in a half-gallon cardboard package was the ideal LFM. Descriptive analysis of 9 commercial LFM followed by consumer acceptance testing was conducted. External preference mapping was conducted with trained panel and consumer acceptance results. Consumer acceptance testing of commercial LFM revealed 3 consumer clusters with distinct preferences for LFM flavor and texture. High sweet taste was a driver of liking for the overall population, and eggy flavor and viscosity were drivers of disliking. Knowledge of consumer preferences for LFM will provide actionable insights for new product development within the dairy industry for this market segment.

## INTRODUCTION

Fluid milk sales have decreased in the United States by 3.7% from 2017 to 2018 (
• Dairy Management Inc.
). According to the United States Department of Agriculture, the total production weight of all fluid milk beverages decreased by nearly 10% from 1987 to 2018 (
• United States Department of Agriculture
November 12, 2019. Economic Research Service. Fluid beverage milk sales quantities by product. Accessed January 7, 2020.
). Although many factors have led to this decline, such as a higher frequency of animal rights activism and vegan diets, one of the largest reasons that consumers avoid dairy is an increase in the diagnosis or perception of lactose intolerance in developed nations (
• Zingone F.
• Bucci C.
• Iovino P.
• Ciacci C.
Consumption of milk and dairy products: Facts and figures.
). It is estimated that more than 70% of the world's population has some degree of lactose intolerance (
• Messia M.C.
• Candigliota T.
• Marconi E.
Assessment of quality and technological characterization of lactose-hydrolyzed milk.
). At over 146 million gallons, lactose-free dairy milk (LFM) currently accounts for 4.0% of the total volume of fluid dairy milk sold in the United States per year, and sales are increasing. Sales of LFM grew by 12% in 2017, with an additional 9% increase in 2018 (
• Dairy Management Inc.
). Although LFM is rising in popularity among consumers, there is little published research on LFM in general, and even less concerning its consumer preferences and purchase habits. In light of the swift rise in the popularity of LFM, it is important to understand consumer desires and perceptions surrounding it to support the dairy industry in providing LFM products that meet the needs of consumers.
• Dooley L.M.
• Chambers IV, E.
• Bhumiratana N.
Sensory characteristics of commercial lactose-free milks manufactured in the United States.
conducted a consumer study on LFM to determine consumer acceptability of LFM compared with traditional milk. That study compared the sensory characteristics of LFM at different fat contents with the corresponding traditional milks using a trained descriptive analysis panel and consumer liking. Lactose-free milk had a more intense sweet taste than traditional milk. In both traditional milk and LFM, consumer liking and fat content of the milk were directly related; as fat content of the milk increased, consumer liking scores also increased (
• Dooley L.M.
• Chambers IV, E.
• Bhumiratana N.
Sensory characteristics of commercial lactose-free milks manufactured in the United States.
). However, a full profile of consumer expectations and perception of LFM was not conducted. Most notably, consumers were not screened to be LFM consumers, but were general milk consumers. The LFM consumer's purchase habits and preferences, such as packaging, pricing, and labeling, were not investigated. These product attributes heavily affect product success in the marketplace (
• McLean K.G.
• Hanson D.J.
• Jervis S.M.
• Drake M.A.
Consumer perception of retail pork bacon attributes using adaptive choice-based conjoint analysis and maximum differential scaling.
;
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
). Additionally, the objective of that study was to compare the sensory properties of LFM and traditional milk, rather than to investigate the specific sensory properties of LFM that lead to consumer acceptance.
While
• Dooley L.M.
• Chambers IV, E.
• Bhumiratana N.
Sensory characteristics of commercial lactose-free milks manufactured in the United States.
is the only LFM consumer study to our knowledge, several studies involving LFM have focused on the activity of β-galactosidase in milk, as well as its chemical effects on the quality of the milk.
• Nielsen S.D.
• Zhao D.
• Le T.T.
• Rauh V.
• Sørensen J.
• Andersen H.J.
• Larsen L.B.
Proteolytic side-activity of lactase preparations.
measured the proteolytic side-activity of β-galactosidase in UHT pasteurized LFM compared with unhydrolyzed UHT milk, and found higher levels of free AA in the lactose-hydrolyzed sample, which indicated that β-galactosidase exhibited proteolytic activity in milk and can lead to a decreased product shelf life (
• Nielsen S.D.
• Zhao D.
• Le T.T.
• Rauh V.
• Sørensen J.
• Andersen H.J.
• Larsen L.B.
Proteolytic side-activity of lactase preparations.
).
• Jansson T.
• Clausen M.R.
• Sundekilde U.K.
• Eggers N.
• Nyegaard S.
• Larsen L.B.
• Ray C.
• Sundgren A.
• Andersen H.J.
• Bertram H.C.
Lactose-hydrolyzed milk is more prone to chemical changes during storage than conventional ultra-high-temperature (UHT) milk.
conducted a similar study with UHT lactose-hydrolyzed and unhydrolyzed milk, but the study was conducted over a 9-mo storage time. They also found β-galactosidase to exhibit signs of proteolysis. Additionally, they observed higher concentrations of furosine and 2-methylbutanal in the lactose-hydrolyzed samples, indicating these samples were more prone to Maillard and other reactions involving sugars (
• Jansson T.
• Clausen M.R.
• Sundekilde U.K.
• Eggers N.
• Nyegaard S.
• Larsen L.B.
• Ray C.
• Sundgren A.
• Andersen H.J.
• Bertram H.C.
Lactose-hydrolyzed milk is more prone to chemical changes during storage than conventional ultra-high-temperature (UHT) milk.
).
• Messia M.C.
• Candigliota T.
• Marconi E.
Assessment of quality and technological characterization of lactose-hydrolyzed milk.
conducted a study focused on measuring Maillard products, such as furosine and lactulose, in lactose-hydrolyzed milk and unhydrolyzed milk. Over time, the concentration of furosine was significantly higher in lactose-hydrolyzed milk compared with traditional milk, with the opposite effect for lactulose. A higher concentration of furosine, which is a heat load indicator, suggests that lactose-hydrolyzed milk is more prone to Maillard reactions than traditional milk. Therefore, lactose-hydrolyzed milk may contain Maillard reaction flavors (such as higher cooked or caramelized flavors) not present in traditional milk with the same heat load.
• Messia M.C.
• Candigliota T.
• Marconi E.
Assessment of quality and technological characterization of lactose-hydrolyzed milk.
concluded that lactose-hydrolyzed milk was less chemically stable than traditional milk, given the greater reactivity of glucose and galactose compared with lactose.
Focus groups represent a popular qualitative data collection method that allows researchers to quickly learn consumer attitudes. Focus groups provide the opportunity to ask open-ended questions to gain useful insights into consumer perceptions. The process encourages the participants to determine the importance of product attributes using their own words and thoughts, enhancing an understanding of consumer perspectives (
• Kitzinger J.
Qualitative research: Introducing focus groups.
). Focus groups also allow participants to interact with each other in open discussions that lead to information that a researcher might not have considered.
In addition to understanding consumer perception of LFM through focus groups, it is also important to identify consumer desires that influence purchase decisions. Conjoint analysis is an analytical technique that simulates this scenario (
• Orme B.K.
). In contrast to the qualitative nature of focus groups, conjoint analysis is a quantitative technique and is often applied to data gathered from an online survey exercise. During a conjoint survey exercise, consumers are presented with hypothetical product concepts randomly generated by the software. Survey participants are requested to choose which hypothetical product concept is most appealing. By having consumers evaluate various product attributes within product concepts, the importance of each product attribute can be assessed. Although there are many different types of conjoint analyses, adaptive choice-based conjoint (ACBC), a variant of traditional choice-based conjoint, has gained popularity and been used in studies both inside and outside of the food industry (
• Oltman A.E.
• Jervis S.M.
• Drake M.A.
Consumer attitudes and preferences for fresh market tomatoes.
;
• Al-Omari B.
• Sim J.
• Croft P.
• Frisher M.
Generating individual patient preferences for the treatment of osteoarthritis using adaptive choice-based conjoint (ACBC) analysis.
;
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
). The ACBC surveys adapt to the specific responses of each individual, creating a personalized survey for each participant (
• Orme B.K.
;
• Jervis S.M.
• Ennis J.M.
• Drake M.A.
A comparison of adaptive choice-based conjoint and choice-based conjoint to determine key choice attributes of sour cream with limited sample size.
). For this reason, ACBC surveys can achieve consistent data with fewer overall participants than choice-based conjoint. Furthermore, ACBC surveys are more reliable when price is included as an attribute (
• Chapman C.N.
• Alford J.L.
• Johnson C.
• Weidemann R.
• Lahav M.
Sawtooth Software Research Paper Series. Accessed April 24, 2020.
;
• Jervis S.M.
• Ennis J.M.
• Drake M.A.
A comparison of adaptive choice-based conjoint and choice-based conjoint to determine key choice attributes of sour cream with limited sample size.
;
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
). Adaptive choice-based conjoint techniques have been applied to many food products including bread, bacon, and fluid milk (
• Jervis M.G.
• Jervis S.M.
• Guthrie B.
• Drake M.A.
Determining children’s perceptions, opinions and attitudes for sliced sandwich breads.
;
• McLean K.G.
• Hanson D.J.
• Jervis S.M.
• Drake M.A.
Consumer perception of retail pork bacon attributes using adaptive choice-based conjoint analysis and maximum differential scaling.
;
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
).
Conjoint analysis survey designs are often paired with other survey methods, including maximum difference (MaxDiff) scaling and Kano questions. The MaxDiff method is similar to conjoint analysis in that it requires participants to make trade-offs, but the trade-offs are based on single attributes or statements, rather than entire product builds. The MaxDiff exercises do not include product concepts with multiple attributes. Consumers are presented with a list of product attributes and are asked to choose the “best” and “worst” options in the list, rather than an ideal or acceptable multi-attribute concept, similar to ACBC. Kano questions are also now fielded via an online survey interface and differ from MaxDiff and ACBC by integrating both qualitative and quantitative data. Kano questions aim to identify consumer attitudes toward certain product attributes based on whether the attribute is present or absent from a product offering (
• Kano N.
• Seraku N.
• Takahashi F.
• Tsuji S.
Attractive quality and must-be quality.
;
• Zacarias D.
The Complete Guide to the Kano Model - Folding Burritos. Accessed Dec. 16, 2019.
). Both Kano questions and MaxDiff have been used in a variety of food studies, including chocolate milk, tomatoes, hot beverages, bacon, and fluid milk (
• Kim M.K.
• Lopetcharat K.
• Drake M.A.
Influence of packaging information on consumer liking of chocolate milk.
;
• Li X.E.
• Lopetcharat K.
• Drake M.
Extrinsic attributes that influence parents’ purchase of chocolate milk for their children.
;
• Oltman A.E.
• Jervis S.M.
• Drake M.A.
Consumer attitudes and preferences for fresh market tomatoes.
;
• McLean K.G.
• Hanson D.J.
• Jervis S.M.
• Drake M.A.
Consumer perception of retail pork bacon attributes using adaptive choice-based conjoint analysis and maximum differential scaling.
;
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
).
While focus groups and survey methods are conceptual exercises, consumer acceptance taste tests require consumers to directly interact with the product. Blinded consumer acceptance tests have been shown to accurately assess consumer liking of foods. In conjunction with trained descriptive analysis data, consumer acceptance data can be used for preference mapping, which provides an understanding of objective sensory attributes that drive liking or disliking (
• Clark J.E.
Taste and flavour: Their importance in food choice and acceptance.
). To our knowledge, a collective examination of the LFM consumer has never been attempted before this study. Because the popularity of LFM is growing quickly in the United States, identifying drivers of liking and purchase habits will be extremely valuable for new product development of lactose-free beverages in the dairy industry. The goal of this study was to apply focus groups, survey methods, and consumer acceptance tests to investigate consumer desires and perceptions of LFM.

## MATERIALS AND METHODS

### Experimental Overview

Three focus groups were conducted to identify consumer perceptions of LFM. We conducted the focus groups first to take advantage of unexpected information that is often gathered through open discussions with consumers. This qualitative exercise was necessary for a deeper understanding of LFM consumer perceptions to assist with development of the subsequent survey and consumer liking test ballot. The online survey was conducted following the focus groups. The online survey consisted of an ACBC exercise, a MaxDiff exercise, and Kano questions. The objective of the online survey was to expand on the information obtained from the focus groups with a quantitative method. It also provided a means to reach many more consumers than would have been possible with focus groups. Additionally, it allowed for a quantitative analysis of the purchase habits and preferences of LFM consumers. Subsequently, 9 commercial 2% LFM products (LFM1 to LFM9) were evaluated by a trained descriptive analysis (DA) panel. Finally, LFM consumers evaluated the same 9 LFM products across a 2-d period. Trained panel DA and consumer acceptance testing of LFM were separate analyses from the focus groups and online survey, and allowed for an understanding of LFM consumer preferences regarding actual sensory perception of commercially available LFM. External preference mapping was then applied with trained panel DA consumer acceptance data to illustrate the sensory properties in LFM that drive liking and disliking among consumers. All sensory and survey testing procedures were conducted in compliance with the North Carolina State University (NCSU) Institutional Review Board regulations.

### Focus Groups

Three 90-min focus groups were conducted on the NCSU campus. Each focus group comprised 7 to 10 adult LFM consumers (n = 25 total). All focus group participants were between the ages of 18 and 64 yr and consisted of 9 men and 16 women. Participants were recruited using a database of over 10,000 consumers maintained by the Sensory Service Center at NCSU. To qualify for the focus groups, consumers had to be between the ages of 18 and 64 yr, earn an annual salary greater than $20,000, and purchase and consume LFM regularly (at least once per month). The focus groups were led by an experienced moderator, while a scribe observed remotely through video and audio online streaming. All focus group sessions were also video recorded for repeat-viewing purposes. During each focus group, the participants were asked a series of questions regarding the following focus areas: (1) Purchase Habits, (2) Packaging and Labeling, (3) Milk Processing, (4) Sensory Characteristics, (5) Milk Applications (Figure 1). All questions and responses were verbal in an open discussion format. For the Milk Processing focus area, questions were asked regarding participant knowledge of this subject. Then, participants were given an information sheet outlining pasteurization and ultrapasteurization processes, and then asked if their perception changed after being educated on the subject. The data gathered from the focus groups were used to generate the online survey. Participants were compensated with a$35 gift card to a local store following participation in one of the focus groups.

### Online Survey

An online survey was created based on the focus group results using Lighthouse Studio (Sawtooth Software version 9.5.3, Orem, UT). The survey was fielded using the same database used for focus group participant recruitment. Consumers over the age of 18 y who had purchased and consumed LFM within the last 6 mo (n = 331) were able to participate in the survey. After answering preliminary screen questions, participants (55 men and 276 women) were directed to MaxDiff scaling, Kano questions, and an ACBC survey specifically focusing on LFM. Upon full completion of the survey, participants were entered into a drawing for gift cards to a local store in the amounts of $100 (1 total),$40 (2 total), and $20 (11 total). The ACBC survey consisted of 9 attributes with 2 to 5 levels per attribute (Table 1). Prices were based on LFM prices in the Raleigh-Durham, North Carolina, area. The survey contained 1 build-your-own (BYO) task and 8 screening tasks with 4 product concepts per task. For each screening task, consumers were asked to choose which of the products presented were “a possibility” or “won't work for me.” To ensure the exercise adapted to each survey participant, 5 unacceptable and 4 must-have questions were integrated into the screening portion, in compliance with the software recommendations ( • Orme B.K. Fine-tuning CBC and adaptive CBC questionnaires. Sawtooth Software Research Paper Series. Accessed April 24, 2020. ). Subsequently, a 10-question choice task tournament section was conducted. Each choice task presented 3 randomly generated LFM product concepts based on the levels and attributes specified in Table 1. The levels within each attribute of the product concepts differed from the participant's BYO product by 2 to 4 attributes. Consumers were asked to choose the “best option” of the 3 product concepts. Twenty was the maximum number of product concepts that could be entered into the tournament. The ACBC exercise concluded after 10 choice tasks. Table 1Attributes and levels used in adaptive choice-based conjoint (ACBC) survey on lactose-free milk PricePackage materialPackage sizeLactose removal methodShelf life Conventional pasteurization = 20 d under refrigeration; ultrapasteurization = 60 d under refrigeration; shelf stable = 9 mo without refrigeration. Sweetness Sweetness compared with regular dairy milk. Texture Texture compared with regular dairy milk. Nutrition claimsLabel claims$2.00/0.5 galCardboardPintLactase enzymeConventional pasteurizationLess sweetThinnerHigh proteinOrganic
$3.00/0.5 galClear plasticQuartFiltration and ultrafiltrationUltrapasteurizationSame sweetnessSameHigh calciumLow carbon footprint$4.00/0.5 galOpaque plasticHalf-gallonShelf stableMore sweetThickerReduced sugarEthically sourced
$5.00/0.5 galGallonNoneGrass fed$6.00/0.5 galNone
1 Conventional pasteurization = 20 d under refrigeration; ultrapasteurization = 60 d under refrigeration; shelf stable = 9 mo without refrigeration.
2 Sweetness compared with regular dairy milk.
3 Texture compared with regular dairy milk.
Preceding the ACBC exercise, consumers participated in MaxDiff scaling and Kano questions. The MaxDiff exercise consisted of 15 LFM attributes and 11 questions with 5 attributes listed per question. Consumers were asked to choose the most important and least important attribute in each set. Kano questions were asked immediately after the MaxDiff scaling exercise. They comprised many of the same features as the ACBC survey, such as questions concerning packaging, flavor, texture, and claims. Consumers were asked all Kano questions in a functional manner (e.g., “LFM that has the same sweetness as regular milk”) and in a dysfunctional manner (e.g., “LFM that does not have the same sweetness as regular milk”). The response options for each LFM attribute were: “I like it,” “I expect it,” “I don't care/neutral,” “I can live with it,” and “I dislike it.”

### Descriptive Analysis

Nine representative commercial 2% LFM products were evaluated by a trained sensory panel. The LFM were purchased on 2 different occasions, 3 wk apart. All LFM were ultrapasteurized. The trained panel consisted of 5 women and 2 men (ages 24–54 yr), each with over 100 h of experience with sensory evaluation of dairy products. Panelists documented sensory attributes of LFM using a 0 to 15 point universal scale consistent with the Spectrum method (
• Meilgaard M.C.
• Civilly G.V.
• Carr B.T.
Descriptive analysis techniques.
) and an established lexicon for milk (
• Lee A.P.
• Barbano D.M.
• Drake M.A.
The influence of ultra-pasteurization by indirect heating versus direct steam injection on skim and 2% fat milks.
;
• McCarthy K.S.
• Lopetcharat K.
• Drake M.A.
Milk fat threshold determination and the effect of milk fat content on consumer preference for fluid milk.
). Milks were evaluated at 20°C in coded 60-mL soufflé cups (PFS Sales Co., Raleigh, NC). The samples were prepared with the overhead lights off to avoid light oxidation. For each of the 2 sessions, panelists were calibrated with a commercial 2% ultrapasteurized milk and a commercial 2% ultrapasteurized LFM. Panelists expectorated samples after each evaluation and rinsed with deionized water. Each milk was evaluated by each panelist in duplicate. Paper ballots were used for data collection.

### Consumer Acceptance Taste Test

Subsequently, LFM consumers (n = 160) were recruited to evaluate the nine 2% LFM evaluated by the trained panel. Consumers consisted of 46 men and 114 women who were 18 to 64 yr old, primary shoppers, and purchased and consumed LFM at least once per month. Each consumer evaluated all 9 LFM samples over a 2-d period. Five LFM were evaluated on the first day and 4 on the second day. The order of presentation for all LFM was balanced and randomized over the 2-d period, meaning each consumer could receive any 5 of the 9 LFM on d 1, and the remaining 4 on d 2, in a randomized and balanced order. Consumers were served 60 mL of each sample in 155-mL clear plastic tumbler (PFS Sales Co.) at 4°C. Samples were coded with 3-digit blinding codes.

### Descriptive Analysis

Sensory differences were documented among the 9 commercial 2% LFMs evaluated (Table 4). The milks differed in all attributes except for milkfat flavor (Figure 4). Four of the milks had distinct paperboard flavor. It is important to note that all milks were packaged in a paperboard carton, except for LFM2. Only 2 milks, LFM2 and LFM4, exhibited eggy flavor. These results were somewhat unexpected considering that ultrapasteurized milk tends to have distinct intensities of eggy flavor due to the increased heat treatment. A recent study also shows that the volatile sulfur compounds that create eggy flavor in milk result from Maillard reactions between reducing sugars (lactose) and cysteine and methionine amino acids (
• Jo Y.
• Benoist D.M.
• Barbano D.M.
• Drake M.A.
Flavor and flavor chemistry differences among milks processed by high-temperature, short-time pasteurization or ultra-pasteurization.
). Considering lactose is hydrolyzed into glucose and galactose, which are both reducing sugars, an increase in eggy flavor might be expected in LFM compared with traditional milk. The only sample that exhibited grassy flavor was LFM6, and this milk was an organic grass fed LFM, so grassy flavor was expected. The milks evaluated were different in viscosity (P < 0.05). The most viscous milks were LFM2 and LFM6, and LFM1 and LFM7 were the least viscous. The most distinguishing attribute between milks was sweet taste intensity, which was responsible for the most variation among milks. The median sweet taste intensity among LFMs evaluated was 3.2, but sweet taste intensities ranged from 1.5 (LFM2) to 4.2 (LFM9). This large variation in sweet taste intensity among the 9 LFMs evaluated is likely due to the starting concentration of lactose in each milk. An important distinction for LFM2 is that it used a combination of lactase enzyme and ultrafiltration to remove lactose, whereas other milks solely used the lactase enzyme. Ultrafiltration would have greatly reduced the starting lactose concentration in this sample, resulting in a largely reduced sweet taste, and likely increased viscosity, cooked flavor intensity, and eggy flavor intensity due to the increase in protein concentration.
Table 4Descriptive analysis trained panel means
Attributes were scored on a 0- to 15-point scale, with 0 = not intense at all and 15 = extremely intense. ND = not detected.
of 9 samples of lactose-free milk (LFM)
AttributeSample
LFM1LFM2LFM3LFM4LFM5LFM6LFM7LFM8LFM9
Overall aroma2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.1
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
Sweet aromatic1.4
Different letters in rows indicate significant differences (P < 0.05).
0.8
Different letters in rows indicate significant differences (P < 0.05).
1.0
Different letters in rows indicate significant differences (P < 0.05).
1.2
Different letters in rows indicate significant differences (P < 0.05).
1.0
Different letters in rows indicate significant differences (P < 0.05).
1.0
Different letters in rows indicate significant differences (P < 0.05).
1.0
Different letters in rows indicate significant differences (P < 0.05).
1.0
Different letters in rows indicate significant differences (P < 0.05).
1.4
Different letters in rows indicate significant differences (P < 0.05).
Cooked4.0
Different letters in rows indicate significant differences (P < 0.05).
4.1
Different letters in rows indicate significant differences (P < 0.05).
4.2
Different letters in rows indicate significant differences (P < 0.05).
4.5
Different letters in rows indicate significant differences (P < 0.05).
4.4
Different letters in rows indicate significant differences (P < 0.05).
4.0
Different letters in rows indicate significant differences (P < 0.05).
4.0
Different letters in rows indicate significant differences (P < 0.05).
3.8
Different letters in rows indicate significant differences (P < 0.05).
4.0
Different letters in rows indicate significant differences (P < 0.05).
Paperboard1.0
Different letters in rows indicate significant differences (P < 0.05).
NDNDNDND1.4
Different letters in rows indicate significant differences (P < 0.05).
1.3
Different letters in rows indicate significant differences (P < 0.05).
1.2
Different letters in rows indicate significant differences (P < 0.05).
ND
EggyND0.9
Different letters in rows indicate significant differences (P < 0.05).
ND0.8
Different letters in rows indicate significant differences (P < 0.05).
NDNDNDNDND
Milkfat2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
GrassyNDNDNDNDND1.0
Different letters in rows indicate significant differences (P < 0.05).
NDNDND
Sweet3.3
Different letters in rows indicate significant differences (P < 0.05).
1.5
Different letters in rows indicate significant differences (P < 0.05).
3.2
Different letters in rows indicate significant differences (P < 0.05).
3.6
Different letters in rows indicate significant differences (P < 0.05).
3.4
Different letters in rows indicate significant differences (P < 0.05).
3.2
Different letters in rows indicate significant differences (P < 0.05).
2.9
Different letters in rows indicate significant differences (P < 0.05).
3.0
Different letters in rows indicate significant differences (P < 0.05).
4.2
Different letters in rows indicate significant differences (P < 0.05).
Astringency1.9
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
1.8
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
1.8
Different letters in rows indicate significant differences (P < 0.05).
Viscosity1.9
Different letters in rows indicate significant differences (P < 0.05).
2.20
Different letters in rows indicate significant differences (P < 0.05).
2.1
Different letters in rows indicate significant differences (P < 0.05).
2.1
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.15
Different letters in rows indicate significant differences (P < 0.05).
1.9
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
2.0
Different letters in rows indicate significant differences (P < 0.05).
a–f Different letters in rows indicate significant differences (P < 0.05).
1 Attributes were scored on a 0- to 15-point scale, with 0 = not intense at all and 15 = extremely intense. ND = not detected.

### Consumer Acceptance Taste Test

The same 9 LFMs evaluated by the trained sensory panel were evaluated by LFM consumers (n = 160) (Table 5). Flavor and sweetness were the most important sensory attributes to LFM consumers, which we concluded because the milks that received the lowest overall liking scores (LFM2 and LFM8) also received the lowest scores for flavor liking and sweetness liking (P < 0.05). Milks that received low overall liking scores also received the lowest quality and purchase intent scores.
Table 5Consumer acceptance liking means for 9 samples of lactose-free milks (LFM; n = 160)
QuestionResponseLFM1LFM2LFM3LFM4LFM5LFM6LFM7LFM8LFM9
Appearance liking
Liking attributes were scored on a 9-point hedonic scale where dislike extremely = 1 and like extremely = 9.
7.2
Different letters in rows following means indicate significant differences (P < 0.05).
7.2
Different letters in rows following means indicate significant differences (P < 0.05).
7.2
Different letters in rows following means indicate significant differences (P < 0.05).
7.2
Different letters in rows following means indicate significant differences (P < 0.05).
7.1
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
7.3
Different letters in rows following means indicate significant differences (P < 0.05).
7.1
Different letters in rows following means indicate significant differences (P < 0.05).
7
Different letters in rows following means indicate significant differences (P < 0.05).
Color liking7.1
Different letters in rows following means indicate significant differences (P < 0.05).
7.1
Different letters in rows following means indicate significant differences (P < 0.05).
7.0
Different letters in rows following means indicate significant differences (P < 0.05).
7.1
Different letters in rows following means indicate significant differences (P < 0.05).
7.0
Different letters in rows following means indicate significant differences (P < 0.05).
6.7
Different letters in rows following means indicate significant differences (P < 0.05).
7.0
Different letters in rows following means indicate significant differences (P < 0.05).
7.0
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
Color JAR
Just-about-right (JAR) questions were scored on a 5-point scale where 1 or 2 = too little, 3 = just about right, and 4 or 5 = too much. Percentage of consumers that selected these options is presented.
(%)
Too light1.3
Different letters in rows following means indicate significant differences (P < 0.05).
7.5
Different letters in rows following means indicate significant differences (P < 0.05).
2.5
Different letters in rows following means indicate significant differences (P < 0.05).
2.5
Different letters in rows following means indicate significant differences (P < 0.05).
1.9
Different letters in rows following means indicate significant differences (P < 0.05).
3.8
Different letters in rows following means indicate significant differences (P < 0.05).
1.9
Different letters in rows following means indicate significant differences (P < 0.05).
3.8
Different letters in rows following means indicate significant differences (P < 0.05).
0.6
Different letters in rows following means indicate significant differences (P < 0.05).
JAR85.6
Different letters in rows following means indicate significant differences (P < 0.05).
85.6
Different letters in rows following means indicate significant differences (P < 0.05).
83.1
Different letters in rows following means indicate significant differences (P < 0.05).
85.6
Different letters in rows following means indicate significant differences (P < 0.05).
86.3
Different letters in rows following means indicate significant differences (P < 0.05).
76.3
Different letters in rows following means indicate significant differences (P < 0.05).
90.6
Different letters in rows following means indicate significant differences (P < 0.05).
83.1
Different letters in rows following means indicate significant differences (P < 0.05).
83.1
Different letters in rows following means indicate significant differences (P < 0.05).
Too dark13.1
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
14.4
Different letters in rows following means indicate significant differences (P < 0.05).
11.9
Different letters in rows following means indicate significant differences (P < 0.05).
11.9
Different letters in rows following means indicate significant differences (P < 0.05).
20.0
Different letters in rows following means indicate significant differences (P < 0.05).
7.5
Different letters in rows following means indicate significant differences (P < 0.05).
13.1
Different letters in rows following means indicate significant differences (P < 0.05).
16.3
Different letters in rows following means indicate significant differences (P < 0.05).
Aroma liking5.7
Different letters in rows following means indicate significant differences (P < 0.05).
5.9
Different letters in rows following means indicate significant differences (P < 0.05).
5.9
Different letters in rows following means indicate significant differences (P < 0.05).
5.9
Different letters in rows following means indicate significant differences (P < 0.05).
5.7
Different letters in rows following means indicate significant differences (P < 0.05).
5.6
Different letters in rows following means indicate significant differences (P < 0.05).
6.1
Different letters in rows following means indicate significant differences (P < 0.05).
5.5
Different letters in rows following means indicate significant differences (P < 0.05).
5.8
Different letters in rows following means indicate significant differences (P < 0.05).
Aroma JAR (%)Too weak20.6
Different letters in rows following means indicate significant differences (P < 0.05).
21.3
Different letters in rows following means indicate significant differences (P < 0.05).
15.6
Different letters in rows following means indicate significant differences (P < 0.05).
21.3
Different letters in rows following means indicate significant differences (P < 0.05).
21.3
Different letters in rows following means indicate significant differences (P < 0.05).
18.8
Different letters in rows following means indicate significant differences (P < 0.05).
29.4
Different letters in rows following means indicate significant differences (P < 0.05).
19.4
Different letters in rows following means indicate significant differences (P < 0.05).
13.8
Different letters in rows following means indicate significant differences (P < 0.05).
JAR59.4
Different letters in rows following means indicate significant differences (P < 0.05).
63.8
Different letters in rows following means indicate significant differences (P < 0.05).
66.3
Different letters in rows following means indicate significant differences (P < 0.05).
64.4
Different letters in rows following means indicate significant differences (P < 0.05).
60.0
Different letters in rows following means indicate significant differences (P < 0.05).
56.3
Different letters in rows following means indicate significant differences (P < 0.05).
61.3
Different letters in rows following means indicate significant differences (P < 0.05).
56.3
Different letters in rows following means indicate significant differences (P < 0.05).
62.5
Different letters in rows following means indicate significant differences (P < 0.05).
Too strong20.0
Different letters in rows following means indicate significant differences (P < 0.05).
15.0
Different letters in rows following means indicate significant differences (P < 0.05).
18.1
Different letters in rows following means indicate significant differences (P < 0.05).
14.4
Different letters in rows following means indicate significant differences (P < 0.05).
18.8
Different letters in rows following means indicate significant differences (P < 0.05).
25.0
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
24.4
Different letters in rows following means indicate significant differences (P < 0.05).
23.8
Different letters in rows following means indicate significant differences (P < 0.05).
Overall liking6.6
Different letters in rows following means indicate significant differences (P < 0.05).
5.5
Different letters in rows following means indicate significant differences (P < 0.05).
6.7
Different letters in rows following means indicate significant differences (P < 0.05).
6.7
Different letters in rows following means indicate significant differences (P < 0.05).
6.6
Different letters in rows following means indicate significant differences (P < 0.05).
5.9
Different letters in rows following means indicate significant differences (P < 0.05).
6.7
Different letters in rows following means indicate significant differences (P < 0.05).
5.4
Different letters in rows following means indicate significant differences (P < 0.05).
6.2
Different letters in rows following means indicate significant differences (P < 0.05).
Flavor liking6.6
Different letters in rows following means indicate significant differences (P < 0.05).
5.4
Different letters in rows following means indicate significant differences (P < 0.05).
6.5
Different letters in rows following means indicate significant differences (P < 0.05).
6.6
Different letters in rows following means indicate significant differences (P < 0.05).
6.5
Different letters in rows following means indicate significant differences (P < 0.05).
5.8
Different letters in rows following means indicate significant differences (P < 0.05).
6.6
Different letters in rows following means indicate significant differences (P < 0.05).
5.3
Different letters in rows following means indicate significant differences (P < 0.05).
6.1
Different letters in rows following means indicate significant differences (P < 0.05).
Flavor JAR (%)Not enough flavor6.9
Different letters in rows following means indicate significant differences (P < 0.05).
46.3
Different letters in rows following means indicate significant differences (P < 0.05).
11.3
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
11.3
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
10.0
Different letters in rows following means indicate significant differences (P < 0.05).
11.3
Different letters in rows following means indicate significant differences (P < 0.05).
8.8
Different letters in rows following means indicate significant differences (P < 0.05).
JAR69.4
Different letters in rows following means indicate significant differences (P < 0.05).
45.0
Different letters in rows following means indicate significant differences (P < 0.05).
67.5
Different letters in rows following means indicate significant differences (P < 0.05).
70.6
Different letters in rows following means indicate significant differences (P < 0.05).
70.0
Different letters in rows following means indicate significant differences (P < 0.05).
50.0
Different letters in rows following means indicate significant differences (P < 0.05).
70.6
Different letters in rows following means indicate significant differences (P < 0.05).
45.6
Different letters in rows following means indicate significant differences (P < 0.05).
60.6
Different letters in rows following means indicate significant differences (P < 0.05).
Too much flavor23.8
Different letters in rows following means indicate significant differences (P < 0.05).
8.8
Different letters in rows following means indicate significant differences (P < 0.05).
21.3
Different letters in rows following means indicate significant differences (P < 0.05).
20.0
Different letters in rows following means indicate significant differences (P < 0.05).
18.8
Different letters in rows following means indicate significant differences (P < 0.05).
43.1
Different letters in rows following means indicate significant differences (P < 0.05).
19.4
Different letters in rows following means indicate significant differences (P < 0.05).
43.1
Different letters in rows following means indicate significant differences (P < 0.05).
30.6
Different letters in rows following means indicate significant differences (P < 0.05).
Sweetness liking6.3
Different letters in rows following means indicate significant differences (P < 0.05).
5.3
Different letters in rows following means indicate significant differences (P < 0.05).
6.3
Different letters in rows following means indicate significant differences (P < 0.05).
6.4
Different letters in rows following means indicate significant differences (P < 0.05).
6.4
Different letters in rows following means indicate significant differences (P < 0.05).
5.8
Different letters in rows following means indicate significant differences (P < 0.05).
6.4
Different letters in rows following means indicate significant differences (P < 0.05).
5.5
Different letters in rows following means indicate significant differences (P < 0.05).
6.1
Different letters in rows following means indicate significant differences (P < 0.05).
Sweetness JAR (%)Not sweet enough8.1
Different letters in rows following means indicate significant differences (P < 0.05).
55.0
Different letters in rows following means indicate significant differences (P < 0.05).
10.6
Different letters in rows following means indicate significant differences (P < 0.05).
12.5
Different letters in rows following means indicate significant differences (P < 0.05).
15.0
Different letters in rows following means indicate significant differences (P < 0.05).
11.3
Different letters in rows following means indicate significant differences (P < 0.05).
10.0
Different letters in rows following means indicate significant differences (P < 0.05).
20.0
Different letters in rows following means indicate significant differences (P < 0.05).
15.6
Different letters in rows following means indicate significant differences (P < 0.05).
JAR61.3
Different letters in rows following means indicate significant differences (P < 0.05).
44.4
Different letters in rows following means indicate significant differences (P < 0.05).
63.1
Different letters in rows following means indicate significant differences (P < 0.05).
64.4
Different letters in rows following means indicate significant differences (P < 0.05).
63.8
Different letters in rows following means indicate significant differences (P < 0.05).
52.5
Different letters in rows following means indicate significant differences (P < 0.05).
63.8
Different letters in rows following means indicate significant differences (P < 0.05).
45.6
Different letters in rows following means indicate significant differences (P < 0.05).
55.6
Different letters in rows following means indicate significant differences (P < 0.05).
Too sweet30.6
Different letters in rows following means indicate significant differences (P < 0.05).
0.6
Different letters in rows following means indicate significant differences (P < 0.05).
26.3
Different letters in rows following means indicate significant differences (P < 0.05).
23.1
Different letters in rows following means indicate significant differences (P < 0.05).
21.3
Different letters in rows following means indicate significant differences (P < 0.05).
36.3
Different letters in rows following means indicate significant differences (P < 0.05).
26.3
Different letters in rows following means indicate significant differences (P < 0.05).
34.4
Different letters in rows following means indicate significant differences (P < 0.05).
28.8
Different letters in rows following means indicate significant differences (P < 0.05).
Cooked milky flavor JAR (%)Not enough flavor6.9
Different letters in rows following means indicate significant differences (P < 0.05).
19.4
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
6.3
Different letters in rows following means indicate significant differences (P < 0.05).
6.3
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
7.5
Different letters in rows following means indicate significant differences (P < 0.05).
8.1
Different letters in rows following means indicate significant differences (P < 0.05).
10.0
Different letters in rows following means indicate significant differences (P < 0.05).
JAR73.1
Different letters in rows following means indicate significant differences (P < 0.05).
61.3
Different letters in rows following means indicate significant differences (P < 0.05).
71.9
Different letters in rows following means indicate significant differences (P < 0.05).
80.0
Different letters in rows following means indicate significant differences (P < 0.05).
75.0
Different letters in rows following means indicate significant differences (P < 0.05).
59.4
Different letters in rows following means indicate significant differences (P < 0.05).
76.9
Different letters in rows following means indicate significant differences (P < 0.05).
50.6
Different letters in rows following means indicate significant differences (P < 0.05).
68.1
Different letters in rows following means indicate significant differences (P < 0.05).
Too much flavor20.0
Different letters in rows following means indicate significant differences (P < 0.05).
19.4
Different letters in rows following means indicate significant differences (P < 0.05).
18.8
Different letters in rows following means indicate significant differences (P < 0.05).
13.8
Different letters in rows following means indicate significant differences (P < 0.05).
18.8
Different letters in rows following means indicate significant differences (P < 0.05).
33.8
Different letters in rows following means indicate significant differences (P < 0.05).
15.6
Different letters in rows following means indicate significant differences (P < 0.05).
41.3
Different letters in rows following means indicate significant differences (P < 0.05).
21.9
Different letters in rows following means indicate significant differences (P < 0.05).
Thickness, mouthfeel, viscosity liking6.8
Different letters in rows following means indicate significant differences (P < 0.05).
6.31
Different letters in rows following means indicate significant differences (P < 0.05).
6.7
Different letters in rows following means indicate significant differences (P < 0.05).
6.7
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
6.6
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
6.29
Different letters in rows following means indicate significant differences (P < 0.05).
6.6
Different letters in rows following means indicate significant differences (P < 0.05).
Thickness, mouthfeel, viscosity JAR (%)Not thick enough12.5
Different letters in rows following means indicate significant differences (P < 0.05).
20.6
Different letters in rows following means indicate significant differences (P < 0.05).
13.8
Different letters in rows following means indicate significant differences (P < 0.05).
20.6
Different letters in rows following means indicate significant differences (P < 0.05).
11.9
Different letters in rows following means indicate significant differences (P < 0.05).
10.6
Different letters in rows following means indicate significant differences (P < 0.05).
10.0
Different letters in rows following means indicate significant differences (P < 0.05).
13.8
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
JAR80.0
Different letters in rows following means indicate significant differences (P < 0.05).
71.9
Different letters in rows following means indicate significant differences (P < 0.05).
79.4
Different letters in rows following means indicate significant differences (P < 0.05).
73.8
Different letters in rows following means indicate significant differences (P < 0.05).
80.6
Different letters in rows following means indicate significant differences (P < 0.05).
73.8
Different letters in rows following means indicate significant differences (P < 0.05).
82.5
Different letters in rows following means indicate significant differences (P < 0.05).
76.9
Different letters in rows following means indicate significant differences (P < 0.05).
81.3
Different letters in rows following means indicate significant differences (P < 0.05).
Too thick7.5
Different letters in rows following means indicate significant differences (P < 0.05).
7.5
Different letters in rows following means indicate significant differences (P < 0.05).
6.9
Different letters in rows following means indicate significant differences (P < 0.05).
5.6
Different letters in rows following means indicate significant differences (P < 0.05).
7.5
Different letters in rows following means indicate significant differences (P < 0.05).
15.6
Different letters in rows following means indicate significant differences (P < 0.05).
7.5
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
Creaminess JAR (%)Not creamy enough10.0
Different letters in rows following means indicate significant differences (P < 0.05).
32.5
Different letters in rows following means indicate significant differences (P < 0.05).
16.9
Different letters in rows following means indicate significant differences (P < 0.05).
18.8
Different letters in rows following means indicate significant differences (P < 0.05).
8.8
Different letters in rows following means indicate significant differences (P < 0.05).
13.8
Different letters in rows following means indicate significant differences (P < 0.05).
15.0
Different letters in rows following means indicate significant differences (P < 0.05).
21.3
Different letters in rows following means indicate significant differences (P < 0.05).
13.1
Different letters in rows following means indicate significant differences (P < 0.05).
JAR84.4
Different letters in rows following means indicate significant differences (P < 0.05).
60.0
Different letters in rows following means indicate significant differences (P < 0.05).
74.4
Different letters in rows following means indicate significant differences (P < 0.05).
70.0
Different letters in rows following means indicate significant differences (P < 0.05).
81.9
Different letters in rows following means indicate significant differences (P < 0.05).
74.4
Different letters in rows following means indicate significant differences (P < 0.05).
76.3
Different letters in rows following means indicate significant differences (P < 0.05).
69.4
Different letters in rows following means indicate significant differences (P < 0.05).
75.6
Different letters in rows following means indicate significant differences (P < 0.05).
Too creamy5.6
Different letters in rows following means indicate significant differences (P < 0.05).
7.5
Different letters in rows following means indicate significant differences (P < 0.05).
8.8
Different letters in rows following means indicate significant differences (P < 0.05).
11.3
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
11.9
Different letters in rows following means indicate significant differences (P < 0.05).
8.8
Different letters in rows following means indicate significant differences (P < 0.05).
9.4
Different letters in rows following means indicate significant differences (P < 0.05).
11.3
Different letters in rows following means indicate significant differences (P < 0.05).
Aftertaste yes (%)43.1
Different letters in rows following means indicate significant differences (P < 0.05).
38.1
Different letters in rows following means indicate significant differences (P < 0.05).
35.6
Different letters in rows following means indicate significant differences (P < 0.05).
39.4
Different letters in rows following means indicate significant differences (P < 0.05).
43.8
Different letters in rows following means indicate significant differences (P < 0.05).
50.0
Different letters in rows following means indicate significant differences (P < 0.05).
44.4
Different letters in rows following means indicate significant differences (P < 0.05).
58.8
Different letters in rows following means indicate significant differences (P < 0.05).
49.4
Different letters in rows following means indicate significant differences (P < 0.05).
Aftertaste liking5.5
Different letters in rows following means indicate significant differences (P < 0.05).
5.0
Different letters in rows following means indicate significant differences (P < 0.05).
6.0
Different letters in rows following means indicate significant differences (P < 0.05).
5.4
Different letters in rows following means indicate significant differences (P < 0.05).
5.9
Different letters in rows following means indicate significant differences (P < 0.05).
5.0
Different letters in rows following means indicate significant differences (P < 0.05).
5.7
Different letters in rows following means indicate significant differences (P < 0.05).
4.4
Different letters in rows following means indicate significant differences (P < 0.05).
5.0
Different letters in rows following means indicate significant differences (P < 0.05).
Quality
Quality was scored on a 5-point scale where 1 or 2 = low quality, 3 = neither high nor low quality, and 4 or 5 = high quality.
3.7
Different letters in rows following means indicate significant differences (P < 0.05).
3.1
Different letters in rows following means indicate significant differences (P < 0.05).
3.6
Different letters in rows following means indicate significant differences (P < 0.05).
3.6
Different letters in rows following means indicate significant differences (P < 0.05).
3.7
Different letters in rows following means indicate significant differences (P < 0.05).
3.4
Different letters in rows following means indicate significant differences (P < 0.05).
3.7
Different letters in rows following means indicate significant differences (P < 0.05).
3.1
Different letters in rows following means indicate significant differences (P < 0.05).
3.4
Different letters in rows following means indicate significant differences (P < 0.05).
Purchase intent
Purchase intent was scored on a 5-point scale where 1 or 2 = would not buy, 3 = may or may not buy, and 4 or 5 = would buy.
3.6
Different letters in rows following means indicate significant differences (P < 0.05).
2.9
Different letters in rows following means indicate significant differences (P < 0.05).
3.6
Different letters in rows following means indicate significant differences (P < 0.05).
3.5
Different letters in rows following means indicate significant differences (P < 0.05).
3.6
Different letters in rows following means indicate significant differences (P < 0.05).
3.1
Different letters in rows following means indicate significant differences (P < 0.05).
3.7
Different letters in rows following means indicate significant differences (P < 0.05).
2.7
Different letters in rows following means indicate significant differences (P < 0.05).
3.2
Different letters in rows following means indicate significant differences (P < 0.05).
a–e Different letters in rows following means indicate significant differences (P < 0.05).
1 Liking attributes were scored on a 9-point hedonic scale where dislike extremely = 1 and like extremely = 9.
2 Just-about-right (JAR) questions were scored on a 5-point scale where 1 or 2 = too little, 3 = just about right, and 4 or 5 = too much. Percentage of consumers that selected these options is presented.
3 Quality was scored on a 5-point scale where 1 or 2 = low quality, 3 = neither high nor low quality, and 4 or 5 = high quality.
4 Purchase intent was scored on a 5-point scale where 1 or 2 = would not buy, 3 = may or may not buy, and 4 or 5 = would buy.
Overall liking scores from the consumer acceptance test were used to segment consumers into distinct clusters based on which milks they most preferred, and each cluster was named accordingly: the sweetness cluster (n = 86), the cooked flavor cluster (n = 42), and the balanced cluster (n = 32) (Figure 5). Similar to the ACBC survey, 3 distinct clusters were identified for the consumer acceptance test population. This was an interesting discovery because the consumers that participated in the ACBC survey and the consumer acceptance test were not necessarily the same, but with the potential for some crossover. These results might indicate that there are truly 3 distinct types of LFM consumers. Based on the consumer segmentation results, the overall liking score for LFM2 was the major factor for determining consumer segmentation.
A high overall liking score for LFM2 was the most distinguishing characteristic of the cooked flavor cluster. Although LFM2 received the lowest overall liking score for the overall population (P < 0.05), the consumers from the cooked flavor cluster gave LFM2 a score of 7.1 in overall liking, their most preferred sample. Conversely, the balanced cluster was mainly characterized by an extremely low overall liking score for LFM2 (2.7). Finally, the sweetness cluster had the highest overall liking score for all LFMs except for LFM2. These results possibly indicate that these consumers either were not able to distinguish samples from one another unless there was a large difference in sweetness (as was the case with LFM2), or they are much more easily pleased than consumers from other clusters. It is important to consider that the sweetness cluster was the largest consumer cluster and constituted most of the consumers that participated in the consumer acceptance test. This might indicate that many more consumers are much more easily pleased by various LFM products, based purely on sensory perception, than would have been anticipated from the focus group and online survey results. For example, the MaxDiff exercise found flavor to be one of the most important LFM attributes that could affect purchase decisions, yet the majority of consumers (the sweetness cluster) were generally pleased by all LFM evaluated. Even LFM2, which received the lowest overall liking score from the sweetness cluster, was still “liked” with an overall liking score of 5.8.
The external preference map from partial least squares regression (Figure 6) helps to further understand distinctions between consumer clusters, based on overall liking scores. As seen in Figure 5, the cooked flavor cluster is largely associated with a high overall liking score for LFM2. Figure 6 illustrates that through their relationship with LFM2, the cooked flavor cluster can also be characterized by preference for LFM that are high in viscosity, eggy flavor intensity, and cooked flavor intensity. One of the most important discoveries learned from Figure 6 is the market potential for LFM products similar to LFM2 among consumers. Lactose-free milk 2 was an ultrafiltered, high protein, low sugar LFM. Although LFM2 received the lowest overall liking score for the overall population (P < 0.05), it is clear that there is market potential for this product in the cooked flavor cluster consumers. Additionally, Figure 6 demonstrates sweetness was a driver of disliking for the cooked flavor cluster, but it was a driver of liking for the sweetness and balanced clusters. The balanced cluster could also be characterized by its drivers of disliking: high viscosity, eggy flavor, and cooked flavor. This can be seen most clearly in Figure 5. The balanced cluster scored LFM2 at 2.7 in overall liking, which was the only milk that possessed these drivers of disliking and lacked sweetness, a driver of liking. Although, sweetness was a driver of liking for the sweetness cluster, this cluster did not appear to a have a specific driver of disliking, especially when considering its overall liking score of LFM2 (Figure 5). The sweetness cluster gave LFM2 a score of 5.8, which was the lowest score given by the sweetness cluster, but still indicated slight liking. The lack of sweetness was likely most responsible for the reduced overall liking score.
An important discovery learned from the consumers acceptance test was the importance of sweet taste to the overall population of LFM consumers as a driver of liking. A good example of this can be seen when comparing LFM2 and LFM4. As seen in Figure 4 and Table 4, LFM2 and LFM4 had a very similar overall sensory profile. The major difference between the 2 milks was their sweet taste intensity. When considering consumer acceptance test results, LFM2 received the lowest overall liking score, while LFM4 received the highest score. Considering these milks were very similar in all other sensory attributes except for sweet taste intensity, the results demonstrated the importance of sweetness to the average LFM consumer. This was an important discovery, especially when considering the findings of the focus groups and online survey, where consumers overwhelmingly stated that they would most prefer LFM that has the same sweetness as traditional milk.
Many of the results of this study reflected those of previous studies on fluid milk. Most notably,
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
noted that price was the most important attribute of traditional fluid milk to consumers making purchase decisions. This principle appears to be even more extreme for LFM consumers, likely due to the higher price point of LFM when compared with traditional fluid milk. Fat content played a large role in purchase decisions for LFM, also confirmed in previous studies with milk (
• McCarthy K.S.
• Parker M.
• Ameerally A.
• Drake S.
• Drake M.
Drivers of choice for fluid milk versus plant-based alternatives: What are consumer perceptions of fluid milk?.
;
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
).
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
also found that flavor of the milk was very important to consumers, even more so than price. This result was also recorded in this study, but only in the MaxDiff exercise. After conducting a consumer acceptance test of ultrapasteurized LFM compared with ultrapasteurized traditional milks,
• Dooley L.M.
• Chambers IV, E.
• Bhumiratana N.
Sensory characteristics of commercial lactose-free milks manufactured in the United States.
found the increased sweetness of LFM compared with traditional milk to be a driver of disliking for consumers, the opposite finding of this study.
• Dooley L.M.
• Chambers IV, E.
• Bhumiratana N.
Sensory characteristics of commercial lactose-free milks manufactured in the United States.
did not specifically recruit LFM consumers to participate in their consumer acceptance test, as this study did. LFM is generally sweeter than traditional milk; therefore, as this study proposes, LFM consumers have likely grown accustomed to the increased sweetness of LFM, and that it has become a driver of liking specifically for LFM consumers. However, fluid milk consumers are generally not used to the sweetness of LFM, and would likely score sweeter LFM lower than traditional milk, as seen in the study by
• Dooley L.M.
• Chambers IV, E.
• Bhumiratana N.
Sensory characteristics of commercial lactose-free milks manufactured in the United States.
.
Consumers have stated many reasons for avoiding dairy milk, including animal rights and vegan diets, but concerns about lactose consumption is one of the largest reasons that cause consumers to avoid dairy milk (
• Zingone F.
• Bucci C.
• Iovino P.
• Ciacci C.
Consumption of milk and dairy products: Facts and figures.
). Lactose-free milk is a good alternative for consumers trying to avoid lactose because it provides a rich source of nutrients that are not provided by plant-based dairy alternatives. On average, plant-based milks supply less than half of the protein found in dairy milk (
• Chalupa-Krebzdak S.
• Long C.J.
• Bohrer B.M.
Nutrient density and nutritional value of milk and plant-based milk alternatives.
). With the exception of soy milk, plant-based milks are not a source of complete proteins. Additionally, calcium found in dairy milk is much more easily absorbed by the body compared with calcium fortified plant-based milk (
• Chalupa-Krebzdak S.
• Long C.J.
• Bohrer B.M.
Nutrient density and nutritional value of milk and plant-based milk alternatives.
). These are examples of the many reasons why plant-based milk alternatives are not recommended as a complete nutritional replacement to dairy milk. When considering that LFM is nutritionally the best alternative to traditional milk for consumers trying to avoid lactose, this makes understanding consumer desires and perceptions of LFM important.

## CONCLUSIONS

Purchasers of LFM are largely comprised of lactose intolerant individuals or individuals who are related to someone lactose intolerant. It is estimated that 70% of the world's population has some degree of lactose intolerance, making the market potential for LFM is significant. Price is the most important attribute of LFM for purchase decisions. Consumers of LFM view the extended shelf life provided by ultrapasteurization as a benefit. They also prefer half-gallon sized cardboard cartons to other options, including gallon sized plastic jugs. Although label and nutrition claims such as “high protein,” “high calcium,” and “organic” were the most popular claims found on a package, they were not important when making the overall purchase decision for LFM. High sweet taste is a driver of liking for the majority of LFM consumers. This is important to consider during future product development and consumer testing for LFM consumers. Although they may think of traditional milk as being the “gold standard” target for LFM, reducing sweetness may lead to reduced consumer acceptance. The drivers of disliking for the overall population of LFM consumers are high viscosity and high eggy flavor intensity. Products with these attributes led to decreased acceptability for the majority of LFM consumers. However, it is important to consider that a segment of the population considers these attributes appealing, indicating a market potential for similar high protein, low carbohydrate milks. While this study presents many insights into LFM consumer perceptions and desires, a limitation is that the focus groups and consumer acceptance test were populated entirely with consumers from the Raleigh and Durham area of North Carolina. It is possible that different regional preferences exist between LFM consumers in the United States. Understanding LFM consumer perceptions, drivers of liking, and drivers of disliking can help to guide product development in the dairy industry to better meet the needs of LFM consumers in today's market.

## ACKNOWLEDGMENTS

Funding was provided in part by the National Dairy Council (Rosemont, IL). The authors have not stated any conflicts of interest.

## REFERENCES

• Dooley L.M.
• Chambers IV, E.
• Bhumiratana N.
Sensory characteristics of commercial lactose-free milks manufactured in the United States.
Lebensm. Wiss. Technol. 2010; 43: 113-118
• Al-Omari B.
• Sim J.
• Croft P.
• Frisher M.
Generating individual patient preferences for the treatment of osteoarthritis using adaptive choice-based conjoint (ACBC) analysis.
Rheumatol. Ther. 2017; 4 (28255898): 167-182
• Chalupa-Krebzdak S.
• Long C.J.
• Bohrer B.M.
Nutrient density and nutritional value of milk and plant-based milk alternatives.
Int. Dairy J. 2018; 87: 84-92
• Chapman C.N.
• Alford J.L.
• Johnson C.
• Weidemann R.
• Lahav M.
Sawtooth Software Research Paper Series. Accessed April 24, 2020.
• Clark J.E.
Taste and flavour: Their importance in food choice and acceptance.
Proc. Nutr. Soc. 1998; 57 (10096128): 639-643
• Dairy Management Inc.
Fluid Milk Retail Report Fluid Milk Retail Report 4–6. Dairy Management Inc., Rosemont, IL2018
• Harwood W.S.
• Drake M.A.
Identification and characterization of fluid milk consumer groups.
J. Dairy Sci. 2018; 101 (30100510): 8860-8874
• Jansson T.
• Clausen M.R.
• Sundekilde U.K.
• Eggers N.
• Nyegaard S.
• Larsen L.B.
• Ray C.
• Sundgren A.
• Andersen H.J.
• Bertram H.C.
Lactose-hydrolyzed milk is more prone to chemical changes during storage than conventional ultra-high-temperature (UHT) milk.
J. Agric. Food Chem. 2014; 62 (25019952): 7886-7896
• Jervis M.G.
• Jervis S.M.
• Guthrie B.
• Drake M.A.
Determining children’s perceptions, opinions and attitudes for sliced sandwich breads.
J. Sens. Stud. 2014; 29: 351-361
• Jervis S.M.
• Ennis J.M.
• Drake M.A.
A comparison of adaptive choice-based conjoint and choice-based conjoint to determine key choice attributes of sour cream with limited sample size.
J. Sens. Stud. 2012; 27: 451-462
• Jo Y.
• Benoist D.M.
• Barbano D.M.
• Drake M.A.
Flavor and flavor chemistry differences among milks processed by high-temperature, short-time pasteurization or ultra-pasteurization.
J. Dairy Sci. 2018; 101 (29501345): 3812-3828
• Kano N.
• Seraku N.
• Takahashi F.
• Tsuji S.
Attractive quality and must-be quality.
J. Japanese Soc. Qual. Control. 1984; 14: 147-156
• Kim M.K.
• Lopetcharat K.
• Drake M.A.
Influence of packaging information on consumer liking of chocolate milk.
J. Dairy Sci. 2013; 96 (23706490): 4843-4856
• Kitzinger J.
Qualitative research: Introducing focus groups.
BMJ. 1995; 311: 299-302
• Lee A.P.
• Barbano D.M.
• Drake M.A.
The influence of ultra-pasteurization by indirect heating versus direct steam injection on skim and 2% fat milks.
J. Dairy Sci. 2017; 100 (28088421): 1688-1701
• Li X.E.
• Lopetcharat K.
• Drake M.
Extrinsic attributes that influence parents’ purchase of chocolate milk for their children.
J. Food Sci. 2014; 79 (24975285): S1407-S1415
• McCarthy K.S.
• Lopetcharat K.
• Drake M.A.
Milk fat threshold determination and the effect of milk fat content on consumer preference for fluid milk.
J. Dairy Sci. 2017; 100 (28088417): 1702-1711
• McCarthy K.S.
• Parker M.
• Ameerally A.
• Drake S.
• Drake M.
Drivers of choice for fluid milk versus plant-based alternatives: What are consumer perceptions of fluid milk?.
J. Dairy Sci. 2017; 100 (28551193): 6125-6138
• McLean K.G.
• Hanson D.J.
• Jervis S.M.
• Drake M.A.
Consumer perception of retail pork bacon attributes using adaptive choice-based conjoint analysis and maximum differential scaling.
J. Food Sci. 2017; 82 (29058811): 2659-2668
• Meilgaard M.C.
• Civilly G.V.
• Carr B.T.
Descriptive analysis techniques.
in: Sensory Evaluation Techniques. 4th ed. CRC Press, Boca Raton, FL2007: 173-186
• Messia M.C.
• Candigliota T.
• Marconi E.
Assessment of quality and technological characterization of lactose-hydrolyzed milk.
Food Chem. 2007; 104: 910-917
• Nielsen S.D.
• Zhao D.
• Le T.T.
• Rauh V.
• Sørensen J.
• Andersen H.J.
• Larsen L.B.
Proteolytic side-activity of lactase preparations.
Int. Dairy J. 2018; 78: 159-168
• Oltman A.E.
• Jervis S.M.
• Drake M.A.
Consumer attitudes and preferences for fresh market tomatoes.
J. Food Sci. 2014; 79 (25219281): S2091-S2097
• Orme B.K.
Fine-tuning CBC and adaptive CBC questionnaires. Sawtooth Software Research Paper Series. Accessed April 24, 2020.
• Orme B.K.
Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research. Research Publishers LLC, Madison, WI2010: 19-50
• United States Department of Agriculture
November 12, 2019. Economic Research Service. Fluid beverage milk sales quantities by product. Accessed January 7, 2020.
• Zacarias D.
The Complete Guide to the Kano Model - Folding Burritos. Accessed Dec. 16, 2019.
• Zingone F.
• Bucci C.
• Iovino P.
• Ciacci C.
Consumption of milk and dairy products: Facts and figures.
Nutrition. 2017; 33 (27727008): 322-325