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.

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.

Near infrared (NIR) is one of the techniques belonging to vibrational spectroscopy. Its radiation (750 to 2500nm) interacts with organic matter, and the absorption spectrum is rich in chemical and physical information of organic molecules. In order to extract valuable information on the chemical properties of samples, it is necessary to mathematically process spectral data by chemometric tools. The most important part in the development of an NIR method is building the predicting model generally called calibration. NIR spectroscopy has several advantages over other analytical techniques: rapidity of analysis, no use of chemicals, minimal or no samples preparation, easily applicable in different work environments (on/in/at line applications). On the other hand, NIR spectroscopy has some disadvantages: low ability to predict compounds at low concentration (<0.1%), necessity of accurate analysis as reference, development of calibration models required high trained personnel, need of a large and up-to... RiassuntoLe analisi chimiche convenzionali sono state impiegate a lungo come unico metodo di riferimento per le determinazioni analitiche dei prodotti agro-alimentari. A partire dalla metà del secolo scorso, una nuovatecnica ha assunto sempre maggiore interesse per i controlli di routine: il NIRS (Near Infrared Spectroscopy).Il vantaggio principale di questa tecnologia è la sua capacità di fornire dei risultati in modo molto rapido e accurato. Essendo, però, un sistema analitico secondario, la tecnologia NIR richiede una calibrazione aggiornata e rappresentativa, creata sulla base di consuete analisi di laboratorio. Nell’ ambito delle scienze animali, il NIR è stato ampiamente impiegato nel settore “feed” per: (I) determinare le componenti nutrizionali principali dei foraggi, insilati e mangimi; (II) stimare la degradabilità della sostanza secca e la digeribilità in vivo, in vitro ed in situ; (III) monitorare le fermentazioni degli insilati; (IV) applicare gli strumenti on\at\in line; (V) garantire la tra...

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.

Prediction performances of portable near infrared instruments for at farm forage analysis