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The objectives of this study were to explore the use of Fourier-transform infrared (FITR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: i) a random cross-validation (CV) [80% calibration (CAL); 20% validation (VAL) set], and ii) a leave-one-out validation (3 farms used as CAL, and the remaining one as VAL set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as “water” and “fingerprint” regions. These findings suggest that, according to the traits studied, the inclusion of “water” regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.
INTERPRETIVE SUMMARY The aims of this study were to assess the feasibility in predicting ovine cheese-making traits by using milk spectra via Fourier-transform infrared (FTIR) spectroscopy, and to quantify the effect of farms on their prediction accuracy. Cross-validation and leave-one-out validation procedures were adopted. Results from the former suggested the potential inclusion of some traits in the routine acquisition of milk spectra from individual sheep milk samples, as a useful alternative to instrumental testing. Conversely, the low prediction accuracies observed for the latter procedure suggest the importance of considering the information related to the farming system.
INTRODUCTION
Sheep dairy products are worldwide common, and, in the last 20 years, the global production of ovine milk and cheese has increased of 27% and 9%, respectively. Indeed, the consequent gain of the gross production value of the sheep dairy chain expanded to more than 7%, which nowadays means a potential international market of about 5 billion current US$ (
). Nevertheless, to guarantee the environmental and economic sustainability and the future of this dairy chain, the entire sector must address climate change and water quality while providing safe products for the world. To achieve this goal, it is essential to provide monitoring tools along the dairy chain that can help to select efficient animals, limit the production waste, and reduce the cost:income ratio. At the dairy industry, the percentage cheese yield (%CY) is one of the most informative cheese-making traits that must be monitored, together with the recovery of nutrients (%REC) from milk in the curd. However, these traits cannot be routinely measured in practice and neither the simulation of cheese-making procedures through laboratory models (
Potential influence of herd and animal factors on the yield of cheese and recovery of components from Sarda sheep milk, as determined by a laboratory bench-top model cheese-making.
The 9-MilCA as a rapid, partly-automated method for simultaneously recording of milk coagulation, curd firming, syneresis, cheese yield, and curd nutrients recovery/whey loss.
). As most of the sheep milk is used to produce cheese, cheese-making traits should be the most appropriate selection goals for dairy breeds to better address the market demand. But the moisture content of cheese is depending especially on cheese-making procedures and on the length and condition of ripening (
), so it is especially the cheese solids, expressed per 100 kg of milk processed (%CYSOLIDS) and per 100 kg milk solids (%RECSOLIDS) to be particularly valuable for the dairy industry. Most of the programs in sheep still select only for total milk yield per lactation while major milk components (i.e., milk fat and protein) are used only in few dairy breeds. In these latter, genetic improvement of %CY is not pursued directly, but by including percentage of milk fat and protein in selection indices (
). Moving along the dairy chain, also at the dairy industry the use of these traits would improve the monitoring of the efficiency of cheese making process, giving a tool to the cheese makers to daily correct the procedures used for cheese production. Both at animal and bulk milk levels, fat and protein are routinely measured via Fourier Transform Infrared (FTIR) spectroscopy, a technique used also to indirectly measure a large number of nutritional characteristics of milk (
The effect of the number of observations used for Fourier transform infrared model calibration for bovine milk fat composition on the estimated genetic parameters of the predicted data.
Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier transform infrared spectra.
). Regarding the prediction of characteristics of milk useful for dairy industry (e.g., coagulation properties, cheese-making), literature for the dairy cattle is massive (
Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy.
Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
Prediction and repeatability of milk coagulation properties and curd-firming modeling parameters of ovine milk using Fourier-transform infrared spectroscopy and Bayesian models.
). So, the possibility to gain information on cheese-making ability of milk in a single-fast and accurate analysis could be advantageous for improving the efficiency along the entire ovine dairy chain. Therefore, the aims of the present study were to investigate the feasibility of using FTIR milk spectra to i) predict %CY and %REC traits, ii) quantify the effect of individual farm on their prediction accuracy, and iii) assess the importance and the potential contribution of single wavelengths to these traits, using individual sheep milk samples.
MATERIALS AND METHODS
Data Collection
Individual milk was collected from 121 ewes reared in 4 different farms of the regional territory of Sardinia, Italy. At each of the 4 farms, ewes were sampled in a single day (one sampling day for each farm). Details of animals and farms were previously reported by
Potential influence of herd and animal factors on the yield of cheese and recovery of components from Sarda sheep milk, as determined by a laboratory bench-top model cheese-making.
. Briefly, ewes were of the Sarda dairy breed, between the first and 8 parity, and 65 to 220 d in milking, milked twice a day (at 6:00 a.m. and 16:00 p.m.) using manually operated milking machines; farms were conducted by following semi-extensive methods. The samples were collected in plastic bottles, immediately refrigerated at 4°C and analyzed within 24 h after collection. Information on farming systems and coagulation ability of the ovine milk of the area is available in previous studies (
For each sample, an aliquot of 100 mL was used to achieve milk composition at the laboratory of the Regional Farmers Association of Sardinia (ARAS, Oristano, Italy). Fat, protein, lactose, total solids (TS) and pH were analyzed using a MilkoScan FT6000 (Foss Electric, Hillerød, Denmark) according to FIL-IDF recommendations calibrated for ovine milk [fat (International Organization for Standardization (ISO) 1211:IDF 1; gravimetric method,
); protein (ISO 8968e2:IDF 20; titrimetric method, Kjeldahl. ISO, 2014); casein (ISO 17997e1:IDF 29; titrimetric method, Kjeldahl. ISO, 2004a); lactose (ISO 5765:IDF 79; enzymatic method. ISO, 2002a); total solids (ISO 6731:IDF 21; determination of total solids content. ISO, 2010b)].
Model Cheese-Making
Another aliquot of 500 mL/ewe was processed at the Laboratory of the DAFNAE Department of the University of Padova (Legnaro, Italy) using a bench-top model cheese-making method. This procedure mimics the production of a fresh, uncooked and artisanal cheese and allows the assessment of %CY and %REC traits
Potential influence of herd and animal factors on the yield of cheese and recovery of components from Sarda sheep milk, as determined by a laboratory bench-top model cheese-making.
. Briefly, each sheep milk sample (500 mL) was heated to 35°C for 30 min, inoculated with a starter culture [freeze-dried formulation of thermophilic lactic bacteria (Delvo-Tec TS-10A DSL; DSM Food Specialties, Delft, the Netherlands), solubilized with skim milk before use and concentrated to 8-fold]. The acidified milk was mixed with a rennet solution at 1.2% wt/vol (Hansen Standard 215, 215 IMCU/mL; with 80 ± 5% chymosin and 20 ± 5% pepsin; Pacovis Amrein AG, Bern, Switzerland; diluted in distilled water to yield 0.051 IMCU mL−1 of milk). Coagulation time was assessed by the visual observation of gelation with the aid of a spoon. After coagulation, each curd obtained from the individual samples was cut into cubes of about 0.5 cm3. Curds were then separated from the whey, drained and pressed, for 18 h at room temperature, using a weight of 1 kg. At the end of the process, each curd and the corresponding whey were weighed and analyzed to achieve composition. Dry matter, fat and protein of the curd were measured by a FoodScan (Foss, Hillerød, Denmark) calibrated according to the following reference methods: dry matter (ISO 8968–1:IDF 20–1; determination of the total solids content. ISO, 2014); fat (ISO 1735:IDF 5; gravimetric method. ISO, 2004c); protein (ISO 5534:IDF 4; determination of nitrogen content, Kjeldahl. ISO, 2004d). Dry matter, fat, protein, and lactose of the whey were analyzed by a MilkoScan FT2 (Foss, Hillerød, Denmark) calibrated according to the following reference methods: total solids (ISO 2920:IDF 58; determination of dry matter. ISO, 2004b); fat (ISO 1854:IDF 59; gravimetric method. ISO, 2008); protein (ISO 8968–1:IDF 20–1; determination of nitrogen content, Kjeldahl. ISO, 2014); lactose (ISO 5765–1:IDF 79–1; enzymatic method. ISO, 2002b). The energy of milk and whey was computed on the basis of the standard values in kilojoules per gram by the NRC (2001; fat: 38.89 kJ × g−1; protein: 23.90 kJ × g−1; lactose: 16.53 kJ × g−1). The following cheese-making traits were obtained from the weights (g) and chemical compositions of milk, curd, and whey (
Potential influence of herd and animal factors on the yield of cheese and recovery of components from Sarda sheep milk, as determined by a laboratory bench-top model cheese-making.
): i) 3%CY traits, namely the yield of fresh curd (%CYCURD), dry matter (%CYSOLIDS) and water retained in curd (as the difference between the milk processed and the whey obtained, %CYWATER) calculated as the respective percentage of weight of curd, dry matter and water out of the processed milk weight; ii) 4%REC traits, namely the recovery of protein (%RECPROTEIN), fat (%RECFAT), solids (%RECSOLIDS) and energy (%RECENERGY) calculated as the respective percentage of the nutrient or energy in the curd out of the corresponding nutrient or energy in the processed milk.
Milk Spectra Storing, Editing and Chemometric Models
For each milk sample, the FTIR spectrum over the range from wavenumbers 5,011 to 925 × cm−1 was collected and recorded as absorbance (A) using the transformation A = log(1/T), where T is the transmission. Two spectra [absorbance (A) using the transformation A = log(1/T); T = transmission], were collected for each sample, and the results were averaged before data analysis.
Before spectra analysis, the raw absorbance values of every wavelength in the FTIR spectra of the milk samples (Figure 1a) were centered and standardized (Figure 1b) to a null mean and a unit sample variance. This procedure reduces the noise along the spectrum mainly due to the water content of milk and allows to boost wavenumbers with potential significant information. To detect outliers, Mahalanobis distances were calculated by means of the Mahalanobis function implemented in the R software (
International Organization for Standardization and International Dairy Federation. Milk and liquid milk products. Determination of fat, protein, casein, lactose and pH content.
). No samples were discarded because all the spectra showed a distance value lower than the mean ± 3 standard deviations. Indeed, the spectra were not subjected to any other mathematical pretreatment.
Figure 1Plots showing the absorbance of raw (a) and standardized (b) milk spectra (solid black line represents the average, whereas the 2 dotted black lines represent the maximum and the minimum of each wavelength, respectively). Spectral regions are identified as short-wavelength infrared (SWIR), short and mid-wavelength infrared (SWIR-MWIR), MWIR-1, MWIR-2, and mid and long-wavelength infrared (MWIR-LWIR).
A Bayesian linear regression was used to predict the %CY and %REC traits. All phenotypes were regressed to the 1,060 FTIR standardized wavelengths under the following model:
where μ is the overall mean, xij are the wavelengths, βj are the regression coefficients and ei the residual with
The BayesB model implemented in the BGLR R package was adopted (
Prediction and repeatability of milk coagulation properties and curd-firming modeling parameters of ovine milk using Fourier-transform infrared spectroscopy and Bayesian models.
Cross-Validation and Leave-One-Out Validation Procedures
The accuracy of the model and the prediction equation were assessed by a random CV and a leave-one-out validation procedure. In the random CV, a calibration (CAL) set (80% of the total records) was used to build the equation, and a validation (VAL) set (20% of the total records) was used to test the model. The samples in the CAL-VAL sets were randomly assigned. To account for the individual milk samples variability, the procedure was repeated 10 times for each trait and results were presented averaging the 10 replicates.
To quantify the effect of the individual farms on predictive ability of the model, a leave-one-out validation procedure was applied, predicting the cheese making traits per each farm in validation at a time. Sheep milk samples from 3 farms were used to build the equation (CAL set), and the remaining samples belonging to one farm were used to test the model (VAL set). The procedure was repeated 4 times, so that all farms were individually evaluated.
The models' performance was assessed by measures of root mean squared error (RMSE), and coefficient of determination (R2), in both CAL and VAL sets (R2CAL and R2VAL, respectively) and by residual predictive deviation (RPD), difference between measured and predicted values (bias), and slope.
For each trait, the equation coefficients corresponding to each wavelength (n = 1,060), the Pearson correlations between each milk absorbance at a given wavelength, and the corresponding measured value of the trait were assessed. The results were plotted along the spectral range and divided into 5 spectral regions, as reported by
Prediction and repeatability of milk coagulation properties and curd-firming modeling parameters of ovine milk using Fourier-transform infrared spectroscopy and Bayesian models.
: short wave infrared (SWIR, often reported in literature as near-infrared), transition between SWIR and mid-infrared (SWIR-MWIR), first and second mid-infrared (MWIR1 and MWIR2, respectively) and transition between mid- and long-infrared (MWIR-LWIR).
Further, the potential contribution of the single wavelengths to the prediction of the investigated trait was defined as the product of the standardized absorbance of each spectrum and the corresponding regression coefficient (β). Then, the percentage contribution of each spectral region was calculated as the sum of the difference, in absolute value, between the maximum and minimum potential contribution of each wavelength falling within the specific region, to the contribution of the entire spectrum, multiplied by 100.
RESULTS AND DISCUSSION
Prediction Accuracy of Cheese-Making Traits from Sheep Milk
Descriptive statistics for %CY and %REC traits (mean ± standard deviation) and results from the cross-validation procedure using individual sheep milk FTIR spectra are summarized in Table 1. The prediction statistics in calibration were reliable for %CY traits, with average high values of R2CAL (from 0.77 to 0.89) and low of RMSECAL (from 0.43 to 1.16). The prediction statistics for %REC traits were similar for %RECSOLIDS and %RECENERGY (R2CAL = 0.81 and 0.72; RMSECAL = 1.81 and 1.78, respectively), moderate for %RECPROTEIN (R2CAL = 0.48, RMSECAL = 1.49), and poor for %RECFAT (R2CAL = 0.21, RMSECAL = 1.36).
Table 1Descriptive statistics of cheese-making traits
Cheese yields (%CY; weight of fresh curd, curd solids, and curd water as percentage of weight of milk processed); milk nutrients recovery in the curd (%REC; weight of the curd component (protein, fat, total solids, energy) to the same component in milk, multiplied by 100);.
obtained by a model cheese making procedure and results (Mean ± SD) from the Cross-Validation procedure using FTIR spectra of individual sheep milk samples
Interval, minimum and maximum of observed values;. 3RPD, Residual predictive deviation.
13.1–28.3
6.77–13.18
6.33–15.22
70.7–81.0
89.9–97.0
50.4–72.1
65.5–83.5
Prediction Statistics, Cross-Validation
NCAL
97
95
96
95
94
95
95
R2CAL
0.86 ± 0.02
0.89 ± 0.01
0.77 ± 0.04
0.48 ± 0.07
0.21 ± 0.03
0.81 ± 0.01
0.72 ± 0.02
RMSECAL
1.16 ± 0.09
0.43 ± 0.02
0.93 ± 0.11
1.49 ± 0.13
1.36 ± 0.04
1.81 ± 0.05
1.78 ± 0.07
NVAL
24
24
24
23
25
24
24
SDVAL
3.24 ± 0.20
1.33 ± 0.12
1.93 ± 0.26
2.18 ± 0.45
1.63 ± 0.18
4.14 ± 0.40
3.30 ± 0.35
R2VAL
0.75 ± 0.11
0.91 ± 0.05
0.59 ± 0.17
0.23 ± 0.12
0.13 ± 0.12
0.86 ± 0.05
0.74 ± 0.06
RMSEVAL
1.61 ± 0.35
0.41 ± 0.12
1.24 ± 0.36
1.94 ± 0.33
1.56 ± 0.17
1.60 ± 0.32
1.69 ± 0.30
RPD4
2.09 ± 0.39
3.49 ± 0.95
1.65 ± 0.39
1.12 ± 0.08
1.04 ± 0.05
2.66 ± 0.43
1.98 ± 0.25
Bias
0.02 ± 1.64
0.04 ± 0.42
0.11 ± 1.29
−0.16 ± 1.96
0.08 ± 1.57
0.09 ± 1.63
0.04 ± 1.72
Slope
1.03 ± 0.15
1.10 ± 0.08
0.95 ± 0.17
1.27 ± 0.61
1.58 ± 1.12
1.10 ± 0.10
1.11 ± 0.13
1 Cheese yields (%CY; weight of fresh curd, curd solids, and curd water as percentage of weight of milk processed); milk nutrients recovery in the curd (%REC; weight of the curd component (protein, fat, total solids, energy) to the same component in milk, multiplied by 100);.
2 Interval, minimum and maximum of observed values;.3RPD, Residual predictive deviation.
The %CY traits showed different prediction accuracies, with the RMSEVAL of 1.61, 0.41, 1.24 and R2VAL of 0.75, 0.91, 0.59, respectively for %CYCURD, %CYSOLIDS, and %CYWATER (Table 1). It is important to consider that the calibration accuracy obtainable with a secondary technique, such as infrared spectroscopy, cannot be greater than the repeatability of the reference measures that is affected by several factors such as the sampling, instrumental features, and the operator. If compared with the statistics reported by
Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
for the prediction of %CYCURD in bovine species, our values were slightly higher in terms of both R2VAL and RMSEVAL (0.71 and 1.44, respectively). This is probably due to the different quality and technological properties of the milk produced and the consequent different average of the cheese making traits between the 2 species (
) as well as to the variability of the data set used.
Regarding the %REC traits, the best prediction accuracies were observed for %RECSOLIDS (R2VAL = 0.86; RMSEVAL = 1.60) and %RECENERGY (R2VAL = 0.74; RMSEVAL = 1.69). The %RECPROTEIN had a very low R2VAL (0.23) and showed the highest RMSEVAL (1.94), but the lowest accuracy in terms of R2VAL was for %RECFAT (0.13). As expected, models with poor performance in the calibration data set, as in the case of the %RECPROTEIN and %RECFAT, produced even poorer predictions in the validation. In this regard, the variability of the reference data in the calibration set and the repeatability of the predicted trait are the most important factors affecting the prediction accuracy (
). Moreover, whereas models predicting the main milk components (i.e., fat, protein) are effective, because the absorbance response is linear to the concentration of the molecules, on the other hand, in the case of minor components (e.g., fatty acids) or indirect traits (e.g., coagulation, cheese-making phenotypes) this may not be true, as the signal in the spectral data is associated to traits having a relationship with the targeted trait. In this case, predictive models may be partly or totally ineffective, because they are predicting molecules having low or no interaction between the chemical element and the infrared rays (
Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
). The FTIR spectroscopy is not expected to detect differences in the recovery of nutrients indeed, but rather in the major components affecting the variability of %REC traits. For example, in ovine milk the %RECPROTEIN and %RECFAT are largely affected by milk protein and to a lesser (e.g., %RECPROTEIN) or no extent (e.g., %RECFAT) by fat (
) than on chemical composition, which could explain the low accuracy of the prediction models. If compared with results from other species, the largest differences in prediction accuracies are mainly ascribed to %RECPROTEIN and %RECFAT. In fact, R2VAL and RMSEVAL values are much lower than the prediction accuracy provided by
Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
in dairy goats (R2VAL = 0.62 and 0.34, and RMSEVAL = 1.55 and 3.28, respectively for %RECPROTEIN and %RECFAT). The dissimilarities observed among these comparisons could be explained by the fact that the relationships among the milk nutrients and their recovery in the curd are different across species, due to the different quantity and proportion of milk components and their physical characteristics. For example,
, investigating the use of FTIR spectroscopy for the detection of milk from different species, found different degree of absorption in the CH2 band, which is related to the acyl chain on fatty acid, as well as for C-O bands related to protein and lactose. The absorption values were higher in sheep milk spectrum compared with that of cow and goat.
In the CV, the slope closest to one (1.03) as well as the lowest bias (0.02) were observed for %CYCURD. Average values of bias ranged from −0.16 of %RECPROTEIN to 0.11 of %CYWATER (Table 1) while their variability for %RECPROTEIN and %RECFAT was quite high (−0.16 and 0.08, respectively; Table 1). Probably this result was much more linked to the different phenotypic variability of these %RECs than their average values. Indeed, it is recognized that %RECPROTEIN is less variable than %RECFAT, and this leads to underfit the predicted data. Similarly, %CYWATER is usually characterized by a higher variability with respect to %CYSOLIDS (
) and, for this reason, the former had the highest bias among %CY traits values were for %RECPROTEIN and %RECFAT (1.27 and 1.58, respectively), suggesting that the degree to which it is possible to predict them is higher than the other traits. These findings further justified additional analyses, as the farm factor is expected to be important for the variability of milk quality (
Effect of the Farm on the Prediction Accuracy of Cheese-Making Traits
The results from the leave-one-out validation procedure are reported in Table 2. The R2VAL values ranged from 0.33 to 0.75 among %CY traits, and from 0.07 to 0.67 for the %REC traits, with very large variability of the range within each trait. As above mentioned, these results confirm that the variability previously found in the CV procedure is actually due also to the individual farm. The RMSEVAL ranged from 0.61 to 1.85 among the %CY traits, and from 1.67 to 2.33 for the %REC traits, again with large variability of the interval range within each single trait. Apparently, it seems that the leave-one-out validation model is not working properly for some traits (e.g., %RECPROTEIN, %RECFAT), but the poor prediction accuracies here are the result of flock specificities (e.g., diets, management) which are different between calibration and validation sets. Our results agree with previous studies investigating cross-validation and external validation strategies (
Cage of covariance in calibration modeling: Regressing multiple and strongly correlated response variables onto a low rank subspace of explanatory variables.
). These findings confirm that the possibility to add information on zootechnical and environmental characteristics related to the farms is fundamental to not incur large predictions errors. Hence, to make those models useable in routine, model robustness should be updated by adding new samples until covering an acceptable proportion of samples when tested under real field conditions.
Table 2Prediction statistics (Mean ± SD) of cheese making-traits
Cheese yields (%CY; weight of fresh curd, curd solids, and curd water as percentage of weight of milk processed); milk nutrients recovery in the curd (%REC; weight of the curd component (protein, fat, total solids, energy) to the same component in milk, multiplied by 100);.
obtained by a model cheese making from the leave-one-out validation procedure using FTIR spectra of individual sheep milk samples
1 Cheese yields (%CY; weight of fresh curd, curd solids, and curd water as percentage of weight of milk processed); milk nutrients recovery in the curd (%REC; weight of the curd component (protein, fat, total solids, energy) to the same component in milk, multiplied by 100);.
2 For R2VAL and RMSE2VAL of leave-one-out validation procedure also interval of 5 farms cross validations was included;.
Importance and Potential Contribution of Single Wavelengths for the Prediction of Cheese-making Traits
In the Figure 2 are reported the prediction equation and simple correlation coefficients for the absorbance of each wavelength of the milk FTIR spectrum for the traits having the best predictive performances (Table 1; %CYCURD, %CYSOLIDS and %RECSOLIDS, respectively). In all the 3 cheese-making traits, the SWIR region was characterized by a constant positive correlation, but still not consistent with any high regression coefficient. This finding agrees with the results found by
investigating the prediction accuracy of cheese-making traits using FTIR milk spectra from dairy goats. Indeed, the SWIR region is not known to contain any absorbance peak specific to a chemical bond found in milk (
Mid-infrared spectroscopy coupled with chemometrics: a tool for the analysis of intact food systems and the exploration of their molecular structure-quality relationships - a review.
), so it would be expected that the contribution of this region to the prediction of cheese-making traits would be close to zero. However, plots showing the potential contribution (e.g., regression coefficients multiplied by the absorbances) of each wavelength for the prediction of cheese-making traits (Figure 3) evidenced that this region accounted for about 10%, 8% and 15% respect to the potential contribution of the entire spectrum for the prediction of %CYCURD (a), %CYSOLIDS (b), and %RECSOLIDS (c), respectively. So, further research is needed to evaluate the value of this region of the milk spectrum.
Figure 2Prediction equation coefficients obtained using the BayesB method (solid green line) and simple correlation coefficients (dashed black line) for the absorbance of each wavelength of the milk FTIR spectrum for the prediction of cheese-making traits (a, %CYCURD = fresh cheese yield; b, %CYSOLIDS = cheese yield in total solids; c, %RECSOLIDS = recovery of total solids). Spectral regions are identified as short-wavelength infrared (SWIR), short and mid-wavelength infrared (SWIR-MWIR), MWIR-1, MWIR-2, and mid and long-wavelength infrared (MWIR-LWIR).
Figure 3Plots showing the potential contribution (maximum and minimum obtained from the multiplication between the standardized absorbances and the prediction equation coefficients, respectively) of each wavelength for the prediction of cheese-making traits (a, %CYCURD = fresh cheese yield; b, %CYSOLIDS = cheese yield in total solids; c, %RECSOLIDS = recovery of total solids). Spectral regions are identified as short-wavelength infrared (SWIR), short and mid-wavelength infrared (SWIR-MWIR), MWIR-1, MWIR-2, and mid and long-wavelength infrared (MWIR-LWIR).
In the SWIR-MWIR region, also known as “water” region, several regression coefficients were recorded across the 3 cheese-making traits, with wavelength peaks close to 0.5 in absolute value for %RECSOLIDS (Figure 2c). On the contrary,
reported an almost null correlation and regression coefficients in this region for goat milk. A possible interpretation of this difference could be linked to the proportion of dry matter respect to the total fresh curd, which is similar between the 2 species, but the quantity of nutrients (and of curd obtained) is higher in sheep compared with the goat. Accordingly, the signals caused by the peaks are stronger in sheep spectrum compared with the goat. Probably, the weakness of these signals in the goat spectrum causes an increase in the noise in turn, that affects the overall prediction accuracy of the model. It is interesting to notice that, in all the 3 cheese-making traits, the region containing most of the peaks was the SWIR-MWIR region (Figure 3). Indeed, it accounted from 25% (%CYSOLIDS) to 30% (%RECSOLIDS) respect of the total potential contribution of the entire spectrum on the traits' prediction, with the most contributing signal at 3,576 wavenumbers × cm−1 for both %CYCURD and %RECSOLIDS. The same wavelength did not show the highest regression coefficient (Figure 2) evidencing the importance of further deepening the relationships between the spectrum peaks and the studied trait. In the case of %CYSOLIDS, the most contributing signal was at 3,260 wavenumbers × cm−1. In addition, as abovementioned, the SWIR-MWIR is often referred to as the main “water” region, so our findings clearly evidence that the inclusion of wavelengths related to the water is fundamental for the prediction of these traits. Similarly,
found wavelengths included between 1,619 and 1,674 cm−1 and 3,073–3,667 wavenumbers × cm−1 characterized by a polymorphism in the DGAT1 gene, known for being associated with milk quality traits (
Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier transform infrared spectra.
in their study on the prediction of methane emission from milk spectra did not observe important signals in either of the 2 water absorption regions of the FTIR spectrum. In that study, the most important signals were concentrated in the MWIR-1 and the MWIR-LWIR regions. So, it may be assumed that the importance of considering the “water” regions depends on the target traits to be predicted and specific applications. For example, using the milk spectra after removing the “water” regions we observed lower R2CAL and RMSECAL values (data not shown) compared with the results in CAL procedure by using the entire spectrum (Table 1) for all the cheese-making traits, except for %CYSOLIDS and %RECSOLIDS, which fitting statistics remained basically identical (data not shown). This means that the information contained in the “water” areas are most likely useful for the prediction of the recovery of single milk components (e.g., fat, protein) rather than their sum (e.g., total solids).
The MWIR-1 region of the milk spectrum is very informative, as it contains the major absorbance peaks for C-H, C = O, C-N, and N-H bonds. The highest regression coefficient for the 3%CY traits were at 2,975 cm−1, around 2,377–2,280 cm−1 and at 1,720 wavenumbers × cm−1 (Figure 2). These areas are linked to the C = C (i.e., to fat), C = N (i.e., amines) and C = O (i.e., aldehydes and ketones) bonds, respectively (
). This region accounted for about 33%, 36% and 29% respect to the total potential contribution of the entire spectrum for the prediction of %CYCURD, %CYSOLIDS and %RECSOLIDS, respectively (Figure 3). In particular, the highest contribution here for %CYCURD and %RECSOLIDS was observed at 2,978 wavenumbers × cm−1, wavelength not previously associated to traits of interests and therefore not yet mentioned in other studies. However, this wavelength is near to others associated to vinyl bond type (e.g., C = CH2;
Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier transform infrared spectra.
). In the MWIR-2 region the 2 highest peaks were at 1,659 wavenumbers × cm−1 for %CYCURD (Figure 2a) and at 1,650 wavenumbers × cm−1 for %RECSOLIDS (Figure 2c), associated with amide I (i.e., 3-turn helix;
) and dienes bonds (i.e., conjugated C-C; Cecchinato and Bittante, 2013), respectively. This region accounted only for about 4% on the total potential contribution for the prediction of all the 3 cheese-making traits (Figure 3).
The MWIR-LWIR region is commonly defined as the “fingerprint” region, hosting several peaks of absorbance associated with C-H, C = C, C-O and N-O bonds (
). The most informative peaks here were around 1,554–1,516 cm−1, at 1,362 cm−1, at 1,234 cm−1 and around 1,030–1,033 wavenumbers × cm−1 in all the 3 cheese-making traits. Wavenumbers belonging to this region have also been associated with other traits in bovine milk, such as CH4, fatty acids (i.e., 18:1 trans-10, 18:1 trans-11, 18:2 cis-9, cis-12;
Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier transform infrared spectra.
Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.
). This region accounted for 24, 27 and 22% respect to the total potential contribution of the entire spectrum for the prediction of %CYCURD, %CYSOLIDS and %RECSOLIDS, respectively (Figure 3), with the most important contribution at 1,543 wavenumbers × cm−1 for both %CYCURD and %RECSOLIDS. This specific wavelength is commonly associated with aromatic nitro compound of milk spectrum (
The high prediction performance of %CYSOLIDS and %RECSOLIDS justify its practical application within the ovine dairy industry. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of %CYCURD, and %RECENERGY, but insufficient for %RECPROTEIN and %RECFAT. The leave-one-out validation procedure showed more modest prediction accuracies, as a result of farm specificities (e.g., diets, management, environment) which were different between calibration and validation sets. In this respect, the inclusion of information on farming system could help to improve the prediction accuracy of these traits. Overall, the largest contribution to the prediction of the cheese-making traits came from the SWIR-MWIR and MWIR-LWIR spectrum regions, supporting the idea that the “water” region is important to keep a high prediction accuracy at times. Further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits.
ACKNOWLEDGMENTS
This research was funded by the “Fondo di Ateneo per la ricerca 2020, Università degli Studi di Sassari” (one-time extraordinary research funding, University of Sassari, Sassari, Italy).
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