Following a comparison of nutrient total-tract digestibility estimates in lactating buffaloes using single-point undigestible NDF (uNDF) or acid-insoluble ash (AIA) as internal markers, the potential of fecal near-infrared spectroscopy (NIRS) to provide calibration equations for the assessment of the chemical composition of feces and nutrient total-tract digestibility estimated with internal markers was explored. Chemical analyses were performed on 147 fecal samples from lactating buffaloes reared in 5 farms in central Italy (Naples). Each farm fed a silage-based total mixed ration (TMR) to the buffaloes, which was sampled in the 2 d before the fecal collection. The TMR and individual fecal samples were collected and analyzed for dry matter (DM), organic matter (OM), ash, AIA, ether extract (EE), starch, fiber fractions (aNDFom, aNDF, ADFom, ADF, hemicellulose, cellulose, ADL, uNDF), N, CP and CP bound to aNDF (NDICP) and to ADF (ADICP). The uNDF content was determined through a 240-h in vitro fermentation and employed, together with AIA as markers to estimate the total-tract apparent digestibility (ttaDe) and total-tract digestibility (ttDe) of DM, OM, ash, N, CP, EE, aNDFom, aNDF, NDIP, ADFom and ADF, ADIN, ADL, hemicellulose, cellulose, starch, non-fibrous carbohydrates (NFC) and fraction B3 of N. No correlation was found between DM and OM digestibility estimated with AIA and uNDF as internal markers. Weak correlations were detected for all the other nutients digestibilities while strong correlations were observed for EE, ADFom, HC, NDIN, ADIN, NB3, NFC and starch.Inizio modulo The sample set (n = 147) was divided in a calibration set (n = 111) and a validation set (n = 36) to "train" and "validate" the fecal NIRS curve through an external validation process. An estimation usable for preliminary or initial evaluation was obtained for N, CP and aNDF fecal content. An excellent prediction was obtained for ttADINDe (R² = 0.90) when estimated with uNDF as internal marker. The NIRS technology was not able to accurately predict all the other traits and the estimated nutrient digestibility of lactating buffalo diets from fecal spectra.

Portable handheld devices based on near-infrared (NIR) technology have improved and are gaining popularity, even if their implementation in milk has been barely evaluated. Thus, the aim of the present study was to assess the feasibility of using short-wave pocket-sized NIR devices to predict milk quality. A total of 331 individual milk samples from different cow breeds and herds were collected in 2 consecutive days for chemical determination and spectral collection by using 2 pocket-sized NIR spectrophotometers working in the range of 740 to 1,070 nm. The reference data were matched with the corresponding spectrum and modified partial least squares regression models were developed. A 5-fold cross-validation was applied to evaluate individual device performance and an external validation with 25% of the dataset as the validation set was applied for the final models. Results revealed that both devices' absorbance was highly correlated but greater for instrument A than B. Thus, the final models were built by averaging the spectra from both devices for each sample. The fat content prediction model was adequate for quality control with a coefficient of determination (R²_ExV) and a residual predictive deviation (RPD_ExV) in external validation of 0.93 and 3.73, respectively. Protein and casein content as well as fat-to-protein ratio prediction models might be used for a rough screening (R²_ExV >0.70; RPD_ExV >1.73). However, poor prediction models were obtained for all the other traits with an R²_ExV between 0.43 (urea) and 0.03 (SCC), and a RPD_ExV between 1.18 (urea) and 0.22 (SCC). In conclusion, short-wave portable handheld NIR devices accurately predicted milk fat content, and protein, casein, and fat-to-protein ratio might be applied for rough screening. It seems that there is not enough information in this NIR region to develop adequate prediction models for lactose, SCC, urea, and freezing point.

Fourier transformed mid-infrared spectroscopy is a widespread method for routine analysis in milk. This method can also be used for the detection of adulterants in bovine milk which represent a current risk for dairy industries and public health. This work focuses on the detection of seven adulterants present in three known concentrations in bulk milk sample. The adulterants were: sodium bicarbonate, sodium chloride, hydroxyproline, glucose, sodium citrate, water, and urea. Partial least squares – discriminant analysis was used to develop statistical models to predict the presence of adulterants in milk samples. The obtained models could provide an easy, efficient, and rapid tool for the dairy industry to detect specific adulterants in milk.

This study evaluated the potential use of mid-infrared spectroscopy to predict milk coagulation traits in bulk milk from Mediterranean Italian buffaloes. A total of 1736 bulk milk samples from 55 farms in central Italy were collected during the official milk quality testing system. The prediction models were developed based on modified partial least-squares regression with 75% of the samples and validated with the remaining samples. All bulk milk samples coagulated between 7.37 and 29.45 min. Average values for milk coagulation traits in the calibration set were 17.71 min, 3.29 min, and 38.83 mm for rennet coagulation time, curd firming time, and curd firmness, respectively. The validation set included samples with similar mean and standard deviation for each trait. The prediction models showed the greatest coefficient of determination of external validation (0.57) and the ratio of prediction to deviation (1.52) for curd firmness. Similar fitting statistics of the prediction models were obtained for rennet coagulation time and curd firming time. In conclusion, the prediction models for all three coagulation traits were below the threshold to consider the prediction models adequate even for rough screening of the samples.

The technological features of milk are essential for cheese manufacturing. This is particularly true for Italy, where most of the milk produced by sheep is intended for cheese production. The possibility to evaluate technological characteristics and coagulation aptitude of milk in advance, before any treatment, is crucial for decision-making at industry level. In the present study, we tested the ability of mid-infrared spectroscopy for prediction of coagulation traits (rennet coagulation time and curd firmness) by using more than 4000 bulk milk samples of 344 sheep herds. The models developed with a partial least square regression showed that spectral data points can be successfully used to predict the two traits. The coefficient of determination in external validation was 0.42 for rennet coagulation time and 0.28 for curd firmness, indicating that sheep milk delivered to dairies can undergo a preliminary screening only to assess the expected coagulation time. This finding will allow manufacturers to evaluate the milk received from farmers. Further investigation will be need to improve the prediction of rennet coagulation time that can be coupled with composition traits to define premiums or penalties in the payment system.