Discriminating muscle type of selected game species using near infrared (NIR) spectroscopy

Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

Identification of individual goat animals by means of near infrared spectroscopy and chemometrics analysis of commercial meat cuts

Although the identification of animal species and muscles have been reported previously, no studies have been found on the use of NIR spectroscopy to identify individual animals from the analysis of commercial meat cuts. The aim of this study was to evaluate the use of a portable near infrared (NIR) instrument combined with classical chemometrics methods [principal component analysis (PCA) and partial least squares discriminant analysis PLS-DA)] to identify the origin of individual goat animals using the spectral signature of their commercial cut. Samples were collected from several carcasses (6 commercial cuts x 24 animals) sourced from a commercial abattoir in Queensland (Australia). The NIR spectra of the samples were collected using a portable NIR instrument in the wavelength range between 950 and 1600 nm. Overall, the PLS-DA models correctly classify 82% and 79% of the individual goat samples using either the goat rack or loin cut samples, respectively. The study demonstrated that NIR spectroscopy was able to identify individual goat animals based on the spectra properties of some of the commercial cut samples analysed (e.g. loin and rack). These results showed the potential of this technique to identify individual animals as an alternative to other laboratory methods and techniques commonly used in meat traceability.

Discrimination of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) binary mixtures using a portable near infrared instrument combined with chemometrics

Consumers demand safe and nutritious foods at accessible prices; where issues associated with adulteration, fraud, and provenance have become important aspects to be considered by the modern food industry. There are many analytical techniques and methods available to determine food composition and quality, including food security. Among them, vibrational spectroscopy techniques are at the first line of defence (near and mid infrared spectroscopy, and Raman spectroscopy). In this study, a portable near infrared (NIR) instrument was evaluated to identify different levels of adulteration between binary mixtures of exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius) and beef (Bos taurus) sourced from a commercial abattoir were used to make different binary mixtures (95 % %w/w, 90 % %w/w, 50 % %w/w, 10 % %w/w and 5 % %w/w) and analysed using a portable NIR instrument. The NIR spectra of the meat mixtures was analysed using principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). Two isosbestic points corresponding to absorbances at 1028 nm and 1224 nm were found to be consistent across all the binary mixtures analysed. The coefficient of determination in cross validation (R²) obtained for the determination of the per cent of species in a binary mixture was above 90 % with a standard error in cross validation (SECV) ranging between 12.6 and 15 %w/w. Overall, the results of this study indicate that NIR spectroscopy can determine the level or ratio of adulteration in the binary mixtures of minced meat.

Evaluating the Use of a Similarity Index (SI) Combined with near Infrared (NIR) Spectroscopy as Method in Meat Species Authenticity

A hand-held near infrared (NIR) spectrophotometer combined with a similarity index (SI) method was evaluated to identify meat samples sourced from exotic and traditional meat species. Fresh meat cuts of lamb (Ovis aries), emu (Dromaius novaehollandiae), camel (Camelus dromedarius), and beef (Bos taurus) sourced from a commercial abattoir were used and analyzed using a hand-held NIR spectrophotometer. The NIR spectra of the commercial and exotic meat samples were analyzed using principal component analysis (PCA), linear discriminant analysis (LDA), and a similarity index (SI). The overall accuracy of the LDA models was 87.8%. Generally, the results of this study indicated that SI combined with NIR spectroscopy can distinguish meat samples sourced from different animal species. In future, we can expect that methods such as SI will improve the implementation of NIR spectroscopy in the meat and food industries as this method can be rapid, handy, affordable, and easy to understand for users and customers.

Characterisation and Identification of Individual Intact Goat Muscle Samples (Capra sp.) Using a Portable Near-Infrared Spectrometer and Chemometrics

Adulterated, poor-quality, and unsafe foods, including meat, are still major issues for both the food industry and consumers, which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near-infrared (NIR) instrument, combined with chemometrics, to identify and classify individual-intact fresh goat muscle samples. Fresh goat carcasses (n = 35; 19 to 21.7 Kg LW) from different animals (age, breeds, sex) were used and separated into different commercial cuts. Thus, the longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900–1600 nm) using a portable NIR instrument. Differences in the NIR spectra of the muscles were observed at wavelengths of around 976 nm, 1180 nm, and 1430 nm, associated with water and fat content (e.g., intramuscular fat). The classification of individual muscle samples was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68–94%) using the second-derivative NIR spectra. The results indicated that NIR spectroscopy could be used to identify individual goat muscles.

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.

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

The potential of Near Infrared Spectroscopy combining different spectral treatments and classification models was assessed for the classification of individual pre-sliced Iberian dry-cured loin packaged under modified atmosphere packaging (MAP) into three official commercial categories (which involve the breed purity and production system) according to the current Iberian Quality Standard (Black, Red and White) and for the assignment to the shelf-life time (0, 4, 8 and 12 months). External validation results provided acceptable results, with up to 100% of samples correctly assigned to Black and White official commercial category and into all storage times. Actually the 100% assignments were obtained using more than one approach (SIMCA and LDA for commercial categories and PLS-DA, SIMCA and LDA for storage times). These results might contribute to support the on-line control of authenticity of official commercial category and to facilitate the monitoring of the quality of pre-package dry-cured products throughout the shelf-life prediction.