Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis. The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60-70%. Conversely, this reduction in accuracy was relatively contained (10-15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts. The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Exploring the use of NIR and Raman spectroscopy for the prediction of quality traits in PDO cheeses

The aims of this proof of principle study were to compare two different chemometric approaches using a Bayesian method, Partial Least Square (PLS) and PLS-discriminant analysis (DA), for the prediction of the chemical composition and texture properties of the Grana Padano (GP) and Parmigiano Reggiano (PR) PDO cheeses by using NIR and Raman spectra and quantify their ability to distinguish between the two PDO and among their ripening periods. For each dairy chain consortium, 9 cheese samples from 3 dairy industries were collected for a total of 18 cheese samples. Three seasoning times were chosen for each dairy industry: 12, 20, and 36 months for GP and 12, 24, and 36 months for PR. A portable NIR instrument (spectral range: 950-1,650 nm) was used on 3 selected spots on the paste of each cheese sample, for a total of 54 spectra collected. An Alpha300 R confocal Raman microscope was used to collect 10 individual spectra for each cheese sample in each spot for a total of 540 Raman spectra collected. After the detection of eventual outliers, the spectra were also concatenated together (NIR + Raman). All the cheese samples were assessed in terms of chemical composition and texture properties following the official reference methods. A Bayesian approach and PLS-DA were applied to the NIR, Raman, and fused spectra to predict the PDO type and seasoning time. The PLS-DA reached the best performances, with 100% correctly identified PDO type using Raman only. The fusion of the data improved the results in 60% of the cases with the Bayesian and of 40% with the PLS-DA approach. A Bayesian approach and a PLS procedure were applied to the NIR, Raman, and fused spectra to predict the chemical composition of the cheese samples and their texture properties. In this case, the best performance in validation was reached with the Bayesian method on Raman spectra for fat (R2VAL = 0.74). The fusion of the data was not always helpful in improving the prediction accuracy. Given the limitations associated with our sample set, future studies will expand the sample size and incorporate diverse PDO cheeses.

Assessment of extra virgin olive oil quality by miniaturized near infrared instruments in a rapid and non-destructive procedure

Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required. This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.

Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese

Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9-30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum-the visible, infrared-A, or infrared-B range-may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.

Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals

Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100-2498, 850-1048, and 350-1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within instrument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.

Identification of Lipid Biomarkers To Discriminate between the Different Production Systems for Asiago PDO Cheese

The lipid fraction of Asiago Protected Designation of Origin (PDO) cheese was analyzed to identify specific biomarkers of its main production systems through a canonical discriminant analysis. The three main production systems of the cheese were considered. Two were located in the upland (UL): pasture-based (P-UL) vs hay-based total mixed rations (H-UL). The third was located in the lowland (LL) and processed milk from cows fed maize silage-based rations (maize silage lowland: MS-LL). The discriminant analysis selected nine fatty acids and vitamin A as lipid biomarkers useful to separate the three production systems. High contents of conjugated linoleic acids, anteiso-C15:0, and vitamin A were discriminant factors for P-UL cheese. The separation between H-UL and MS-LL cheese was less marked with the former having the higher content of conjugated linoleic acids and some polyunsaturated n-6 fatty acids and with the latter being identified by cyclopropane fatty acid and C9:0.

Discrimination between Shiraz wines from different Australian regions: the role of spectroscopy and chemometrics

This study reports the use of UV-visible (UV-vis), near-infrared (NIR), and midinfrared (MIR) spectroscopy combined with chemometrics to discriminate among Shiraz wines produced in five Australian regions. In total, 98 commercial Shiraz samples (vintage 2006) were analyzed using UV-vis, NIR, and MIR wavelength regions. Spectral data were interpreted using principal component analysis (PCA), linear discriminant analysis (LDA), and soft independent model of class analogy (SIMCA) to classify the wine samples according to region. The results indicated that wine samples from Western Australia and Coonawarra can be separated from the other wines based on their MIR spectra. Classification results based on MIR spectra also indicated that LDA achieved 73% overall correct classification, while SIMCA 95.3%. This study demonstrated that IR spectroscopy combined with chemometric methods can be a useful tool for wine region discrimination.

Subacute rumen acidosis in lactating cows: an investigation in intensive Italian dairy herds

Subacute rumen acidosis (SARA) represents one of the most important metabolic disorders in intensive dairy farms that affects rumen fermentations, animal welfare, productivity and farm profitability. The aim of the present study was to study the occurrence of SARA in intensive Italian dairy herds and to determine the relationship between diet composition, ruminal pH and short chain fatty acids (SCFA) concentration. Ten commercial dairy herds were investigated; twelve cows in each herd were selected randomly among animal without clinical signs of disease, with good body condition and between 5 and 60 day-in-milk (DIM), to perform rumenocentesis and obtain rumen fluid. Ruminal pH was determined immediately after sampling and concentration of SCFA in ruminal fluid was determined on samples after storage. An other objective of this research was to study in detail the effects of rumenocentesis on animal health: this study could confirm the extreme validity of this technique as ruminal sampling. Results were subject to anova and correlation analysis using SIGMA STAT 2.03. The results indicated the presence of SARA in three herds (more than 33% cows with rumen pH < 5.5), a critical situation (more than 33% cows with rumen pH < 5.8) in five farms and a normal rumen pH condition in two herds. In particular, dairy herds show on average SCFA concentration of 150, 145, 123 mmol/l for low pH, critical pH and normal pH herds respectively. There were not significant differences among diet composition even if herds with SARA showed a light discordance between initially chemistry composition and residual feed. In the affected herds it was not possible to understand the exact causes of SARA. Animal management seems to be one of the most important factors in developing SARA including total mixed ration preparation.

Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources

The objective of this study was to evaluate near-infrared reflectance spectroscopy (NIRS) as a tool to predict the physicochemical composition of breast meat samples of laying hens fed 4 different diets, a control and 3 diets enriched with different sources of n-3 polyunsaturated fatty acids: marine origin, extruded linseed, and ground linseed. Furthermore, NIRS was used as a tool to classify meat samples according to feeding regimen. Samples were analyzed chemically for DM, ash, protein, lipids, and fatty acid profile. Absorption spectra were collected in diffuse reflectance mode between 1,100 and 2,498 nm every 2 nm. The calibration results for the 72 meat samples were accurate in predicting DM, protein, lipids, and major fatty acids. Poor results were obtained for the calibration equations for ash, pH, color, and lipid oxidation parameters. Partial least squares discriminant analysis was developed to differentiate the breast meat samples that originated from hens fed the different diets. The performance of the discriminant models showed 100% correct classification between the control and the enriched diets. It was concluded that NIRS could be used for quality control predicting chemical composition of poultry meat and possibly some dietary treatments applied to the chickens.

The use of near-infrared reflectance spectroscopy in the prediction of the chemical composition of goose fatty liver

The use of near-infrared reflectance spectroscopy (NIRS) on a meat product is described in this report. The aim of the study was to develop calibration equations to predict the chemical composition of goose fatty liver (foie gras) with lipid contents greater than 40% of the fresh pate. Spectra of 52 foie gras samples were collected in the visible and NIR region (400 to 2,498 nm). Calibration equations were computed for DM, CP, lipids and fatty acids using modified partial least-squares regression. R2 values were high for the total lipid content (0.805) and DM (0.908) but were low for ash (0.151) and relatively low for protein content (0.255). For the major fatty acids, R2 ranged from 0.886 for palmitic acid to 0.988 for oleic acid. Oleic acid, the main fatty acid of the liver, and the stearic acid had higher R2 values than the less represented fatty acids. This study suggests that the NIRS technique can be used to predict lipid content and the fatty acid composition of goose fatty livers, but calibration must be built on a larger number of samples to generate accurate predictions.

The use of near infrared analysis for in situ studies

This study evaluated the use of near infrared reflectance spectroscopy to estimate the chemical composition of feed residues after in situ ruminal incubation. Residues of three alfalfa hays (n = 93) and three alfalfa pellets (n = 93) obtained after ruminal exposure were analyzed for dry matter, crude protein, neutral detergent fiber, acid detergent fiber, and acid detergent lignin by wet chemistry and were also scanned with a near infrared monochromator instrument. A calibration was calculated that combined hay and pellet samples (n = 60). Validation tests were performed using the remaining feed residues. The coefficients of determination and standard errors (percentage of dry matter) of the validation tests for crude protein, neutral detergent fiber, acid detergent fiber, and lignin were of 0.95 and 0.92, 0.96 and 1.68, 0.95 and 1.56, and 0.99 and 0.48, respectively. Similar statistics were obtained using the SELECT algorithm of sample selection; a further 30% reduction was observed in the number of samples that were used for calibration. Kinetics of ruminal degradation and effective degradabilities that were calculated based on chemical composition of the residues as determined by wet chemistry or estimated by near infrared reflectance spectroscopy were not significantly different for 68 out of 71 means. Differences in ruminal kinetics caused by the different forages were also unaffected by method of residue analyses. Near infrared reflectance spectroscopy allowed a reduction in the number of necessary laboratory analyses of feed residues without affecting the results of in situ studies.

Investigation of a LOCAL Calibration Procedure for near Infrared Instruments

A new procedure (LOCAL) for local calibrations is presented. LOCAL selects spectra from a library of samples and computes a PLS calibration equation for each constituent of the sample. This study evaluated the performances of LOCAL on the prediction of ground corn grain and haylage using several different combinations of data transformations, wavelength segment reduction, number of PLS factors and samples used in calibration. LOCAL resulted in lower SEP values for all the constituents of corn and dry matter of haylage with improvements ranging between 6 to 13%. Global calibrations had only a small advantage over LOCAL (1–2%) in the prediction of acid detergent fibre and crude protein in haylage. The two most important variables controlling the accuracy of predictions were number of samples in the calibration and number of PLS factors in the solution. Best results were obtained using 150 to 225 samples and more than 20 PLS factors per calibration equation. The speed of the LOCAL procedure is 0.5–2 s per sample on a 90 MHz computer. With this speed and accuracy, LOCAL is now available for real-time routine operation on a Windows platform.

A blend of animal and cereal protein or fish meal as partial replacement for soybean meal in the diets of lactating Holstein cows

Six replications in Experiment 1 and four replications in Experiment 2 of a 3 x 3 Latin square arrangement of treatments were used to compare soybean meal or soybean meal partially replaced with fish meal or a protein blend for response in intake, milk yield and composition, ruminal NH3 N, blood urea, and ruminal fermentation in lactating Holstein cows. The blend contained 30% corn gluten meal, 30% poultry by-products, 30% blood meal, and 10% feather meal. Periods were 28 d, and the first 7 d were used for adjustment. In addition to these protein sources, diets contained corn silage, alfalfa haylage, dried cracked corn, ground barley plus added fat, and a mineral and vitamin mixture. In Experiment 1, mean DMI was 24.4 kg, mean milk yield was 36.7 kg, mean fat percentage was 3.48%, and mean milk protein percentage was 3.06%; there were no significant differences. In Experiment 2, DMI was different for soybeans (22.6 kg) versus other sources (21.4 kg), but milk yield (32.1 kg) and fat (3.39%) and protein (2.87%) percentages did not differ among diets. In Experiment 1, ruminal NH3 N was greatest for cows consuming soybean diets (11.0 mg/dl) and lowest for cows consuming diets containing the protein blend (8.7 mg/dl). No differences in VFA were found. The lack of response to RUP can be explained by a rather high intake of a fermentable diet, which supplied sufficient absorbable AA according to the Cornell AA model.

Intake, site, and extent of nutrient digestion of lactating cows grazing pasture

The objective of this study was to determine intake and site and extent of nutrient digestion of lactating cows grazing pasture with or without energy supplementation. Four dual-cannulated (rumen and proximal duodenum) cows were randomly assigned to two groups to graze mixed cool season grass legume pasture with either no supplement or with 6.4 kg of cracked corn and mineral mix daily in a switchback design with three 2-wk periods. Markers (Cr2O3 and Co-EDTA) were used to estimate intake, duodenal flow, fecal output, and fractional rates of passage from the rumen. Daily OM intake was similar between diets, but OM intake of pasture was lower when cows were fed corn. Apparent OM and NDF digestibilities in the rumen and total digestive tract were lower when cows were supplemented with corn than when they consumed pasture only. Supplemental corn decreased ruminal NH3 N (22 vs. 17 mg/dl) and increased N recovery at the duodenum (86% vs. 75% of N intake). Nonammonia, nonmicrobial N flowing to the duodenum was 67% of the total NAN flow. Corn increased energy intake of grazing cows, but decreased herbage intake and digestibility.

In situ ruminal disappearance of essential amino acids in protein feedstuffs

Four protein sources were incubated in situ to estimate AA disappearance. Bags containing either soybean meal, corn gluten meal, herring meal, or meat meal were washed in water or suspended in the rumen of two Holstein cows for 8, 12, 16, 24, 48, 72, and 120 h. Cytosine, a bacterial marker for microbial contamination, was used to correct the essential AA profile for microbial contribution to determine the residual essential AA composition of the protein sources after incubation. Ruminal disappearance of individual essential AA was different among feedstuffs. Relative to original feed protein, soybean meal and corn gluten meal decreased the concentration of specific essential AA in the RUP. Concentration of all essential AA, except Arg and His, increased in undegraded meat meal protein. The difference between original and residual AA concentrations in herring meal approached statistical significance. Use of the original AA profile of the feed protein to predict essential AA available for absorption is not accurate because accuracy differs with sources.