Near infrared spectroscopy (NIRS) as tool for classification into official commercial categories and shelf-life storage times of pre-sliced modified atmosphere packaged Iberian dry-cured loin

Effectiveness of handheld near infrared spectrometer for traceability of Angus steaks

The feasibility of Near Infrared Spectroscopy was assessed for aging traceability of steaks of Angus beef (Biceps femoris) individually vacuum-packaged, as well as for the prediction of the refrigeration storage time (0, 7, and 14 days). For this purpose, a total of 288 steaks homogeneously distributed among the sampling times were used. The model developed by Partial Least Squares-Discriminant Analysis offered high discrimination ability between aged beef vs. non-aged. The accuracy after external validation exceeded 90%. Regarding the predictive capacity of the storage time, it was greater on the set of aged samples, in which the accuracy achieved values higher than 96%, while the accuracy decreased to 75% for the non-aged samples. Results obtained support the ability of NIRS technology to be considered in any digital transformation strategy for traceability across the meat supply chain.

Identifying Meat from Grazing or Feedlot Yaks Using Visible and Near-infrared Spectroscopy with Chemometrics

The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5-year-old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400-2500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.

The ability of a portable near infrared instrument to evaluate the shelf-life of fresh and thawed goat muscles

The objective of this study was to evaluate the use of a portable near infrared (NIR) instrument to monitor the shelf-life of four goat muscles [longissimus thoracis et lumborum (LTL), semimembranosus (SM), semitendinosus (ST) and biceps femoris (BF)] stored for up to 8 days (4 °C). The NIR spectra of the muscle samples were collected at day 0, and after 1, 4 and 8 days of storage using a MicroNIR instrument (900-1600 nm). The coefficient of determination in cross-validation (R2) and the standard error in cross validation (SECV) obtained for the prediction of days of storage ranged between 0.76 and 0.86, where the SECV ranged from 0.32 to 0.41. The best statistics in cross-validation were obtained for the prediction of days of storage in the BF samples, followed by the ST and LTL muscles. Differences in the PLS loadings for the cross-validation models were observed due to the interactions between the different muscle samples and days of storage. Overall, these results showed the potential of NIR spectroscopy to identify the time of storage in four different goat muscles. Similar data and techniques could be used to predict the remaining shelf life of meat derived from different species under storage. This information can then be used as a tool to predict and guarantee the safety of meat samples to the consumer along the meat supply and value chains.

NIRS (Near Infrared Spectroscopy) classification of sliced Duroc dry-cured ham under various packaging systems and storage temperature and time

The potential of Near Infrared Spectroscopy (NIRS) was assessed for storage temperature discrimination (4 °C ± 2 vs. 20 °C ± 2) and for the prediction of the length of time that sliced Duroc dry-cured ham was in storage, considering the following packaging types; vacuum (n = 133) and modified atmosphere (MAP) (n = 133), without opening the package. The models, obtained by means of Partial least squares-discriminant analysis, indicated successful classification of the product according to storage temperature after validation (accuracy values of 100.00% in vacuum and between 92.00 and 100% in MAP). Furthermore, good accuracy was obtained for the assignments into storage times, with values comprised between 92.31% and 100.00% for samples under vacuum and between 91.00% and 97.00% for those under MAP, in both cases after validation. Thus, NIRS technology could help to support the preservation temperature traceability and the stocks of sliced dry-cured hams.

Development of a Portable Near-Infrared Spectroscopy Tool for Detecting Freshness of Commercial Packaged Pork

Real-time monitoring of meat quality requires fast, accurate, low-cost, and non-destructive analytical methods that can be used throughout the entire production chain, including the packaged product. The aim of this work was to evaluate the potential of a portable near-infrared (NIR) spectroscopy tool for the on-site detection of freshness of pork loin fillets in modified atmosphere packaging (MAP) stored on display counters. Pork loin slices were sealed in MAP trays under two proportions of O2/CO2/N2: High-Ox-MAP (30/40/30) and Low-Ox-MAP (5/20/75). Changes in pH, color, thiobarbituric acid reactive substances (TBARS), Warner−Bratzler shear force (WBSF), and microbiology (total viable counts, Enteriobacteriaceae, and lactic acid bacteria) were monitored over 15 days post-mortem at 4 °C. VIS-NIR spectra were collected from pork fillets before (through the film cover) and after opening the trays (directly on the meat surface) with a portable LABSPEC 5000 NIR system in diffuse reflectance mode (350−2500 nm). Quantitative NIR models by partial least squares regression (PLSR) showed a promising prediction ability for meat color (L*, a*, C*, and h*) and microbiological variables (R2VAL > 0.72 and RPDVAL > 2). In addition, qualitative models using PLS discriminant analysis obtained good accuracy (over 90%) for classifying pork samples as fresh (acceptable for consumption) or spoiled (not acceptable) based on their microbiological counts. VIS-NIR spectroscopy allows rapid evaluation of product quality and shelf life and could be used for on-site control of pork quality.

Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito

The time lapse between the moment Montanera dry-cured products become available in the market and the consumers purchase could be overcome by freezing the raw meat prior to its curing technological process, i.e. the pre-cure freezing. This study is an attempt to assess the suitability of using Near Infrared Spectroscopy (NIRS) for the pre-cure freezing discrimination in Montanera Iberian dry-cured lomitos (the commercial name given to the Iberian dry-cured presa, Serratus ventralis muscle). The best fitting models developed through Partial Least Square-Discriminant Analysis (PLS-DA) offered a highly-discriminatory capacity, with sensitivity and specificity over 85%. The classification performance decreased in Soft Independent Modelling of Class Analogies (SIMCA) models due to the decrease of the specificity. These findings suggest that NIRS technology in combination with PLS-DA, may be useful for the control of the pre-cure freezing practice.

Near-Infrared Spectroscopy (NIRS) as a Tool for Classification of Pre-Sliced Iberian Salchichón, Modified Atmosphere Packaged (MAP) According to the Official Commercial Categories of Raw Meat

This study evaluates near-infrared spectroscopy (NIRS) feasibility in combination with various pre-treatments and chemometric approaches for pre-sliced Iberian salchichón under modified atmosphere (MAP) classification according to the official commercial category (defined by the combination of genotype and feeding regime) of the raw material used for its manufacturing (Black and Red purebred Iberian and Iberian × Duroc crossed (50%) pigs, respectively, reared outdoors in a Montanera system and White Iberian × Duroc crossed (50%) pigs with feed based on commercial fodder) without opening the package. In parallel, NIRS feasibility in combination with partial least squares regression (PLSR) to predict main quality traits was assessed. The best-fitting models developed by means of partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) yielded high discriminant ability and thus offered a tool to support the assignment of pre-sliced MAP Iberian salchichón according to the commercial category of the raw material. In addition, good predictive ability for C18:3 n-3 was obtained, which may help to support quality control.