Performance of benchtop and portable spectroscopy equipment for discriminating Iberian ham according to breed

Iberian ham is a highly appreciated product and according to Spanish legislation different labels identify different products depending on the genetic purity. Consequently, "100% Iberian" ham from purebred Iberian animals is more expensive than "Iberian" ham from Iberian x Duroc crosses. The hypothesis of this study was that to avoid labelling fraud it is possible to distinguish the breed (Iberian or Iberian x Duroc) of acorn-fed pigs of Iberian ham without any prior preparation of the sample by using spectroscopy that is a rapid and reliable technology. Moreover, portable devices which can be used in situ could provide similar results to those of benchtop equipment. Therefore, the spectra of the 60 samples (24 samples of 100% Iberian ham and 36 samples of Iberian x Duroc crossbreed ham) were recorded only for the fat, only for the muscle, or for the whole slice with two benchtop near-infrared (NIR) spectrometers (Büchi NIRFlex N-500 and Foss NIRSystem 5000) and five portable spectrometers including four portable NIR devices (VIAVI MicroNIR 1700 ES, TellSpec Enterprise Sensor, Thermo Fischer Scientific microPHAZIR, and Consumer Physics SCiO Sensor), and one RAMAN device (BRAVO handheld). The results showed that, in general, the whole slice recording produced the best results for classification purposes. The SCiO device showed the highest percentages of correctly classified samples (97% in calibration and 92% in validation) followed by TellSpec (100% and 81%). The SCiO sensor also showed the highest percentages of success when the analyses were performed only on lean meat (97% in calibration and 83% in validation) followed by microPHAZIR (84% and 81%), while in the case of the fat tissue. Raman technology showed the best discrimination capacity (96% and 78%) followed by microPHAZIR (89% and 81%). Therefore, spectroscopy has proved to be a suitable technology for discriminating ham samples according to breed purity; portable devices have been shown to give even better results than benchtop spectrometers.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

Handheld Devices for Food Authentication and Their Applications: A Review

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

Prediction of carcass composition and meat and fat quality using sensing technologies: A review

Consumer demand for high-quality healthy food is increasing,  thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

Performance of near-infrared spectroscopy in pork shoulder as a predictor for pork belly softness

Pork belly quality indicators are economically relevant in the pork industry. Near-infrared (NIR) spectroscopy of the pork shoulder outer subcutaneous fat layer, belly flop angle, and subjective softness scores of the pork belly were measured (N = 144) to determine the accuracy of pork shoulder NIR spectroscopy as a predictor of pork belly softness. The NIR spectra hot carcass estimates explained over 80.0% variability in pork belly softness (80.5%–90.8%), with low prediction error, suggesting that NIR spectroscopy measured in the pork shoulder is an efficient and accurate indicator of pork belly softness to classify pork bellies for specific market demands.

Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approaches

This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial kernel (R-SVR). Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS and R-SVR with and without wavelength selection based on genetic algorithms (GAs). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation of 4.8.

Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches

This study evaluated visible and near-infrared spectroscopy (Vis-NIRS) to authenticate barley-finished beef using different discrimination approaches. Dietary grain source (barley, corn, or blend-50% barley/50% corn) did not affect (P > 0.05) meat quality but influenced (P < 0.05) fatty acid profiles. The longissimus thoracis (LT) from barley-fed steers had lower n-6 fatty acid content and n-6/n-3 ratio compared to LT from corn and blended grain-fed steers. Vis-NIRS coupled with partial least square discriminant analysis (PLS-DA) and support vector machine in the linear (L-SVM) kernel classified with approximately 70% overall accuracy subcutaneous fat and intact LT samples, respectively, from barley, corn, and blended-fed cattle. When only barley and corn samples were considered, fat and intact LT samples were correctly classified with overall accuracy >94% with PLS-DA and radial/L-SVM, and approximately 90% with PLS-DA and L-SVM, respectively. Ground LT samples were classified with ≤70% overall accuracy. Vis-NIRS measurements on fat and intact LT have potential to discriminate between corn and barley-fed beef.

Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the "time from slaughter" parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

Potential of near infrared (NIR) spectroscopy and dual energy X-ray absorptiometry (DXA) in predicting pork belly softness

Pork bellies (n = 198) were scanned with dual energy X-ray absorptiometry (DXA). Visible and near-infrared reflectance (Vis-NIR) spectra were collected from the lean (latissimus dorsi), subcutaneous fat and intermuscular fat layers. Belly-flop angle and subjective belly scores were collected as measures of pork belly softness. Vis-NIR spectra from a single fat layer could explain between 72.7 and 81.1% of the variation in pork belly softness (43.6-72.4% in validation set). The combination of the lean and subcutaneous layers improved the calibration model fit to 79.7-99.9% (66.3-71.5% in validation set). The DXA estimates explained 62.3% of variation in pork belly softness (65.2% in validation set). Results indicated that DXA and NIR technologies could potentially be utilized for pork belly softness sorting in the pork industry.