Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

Recent Advances in Light Penetration Depth for Postharvest Quality Evaluation of Fruits and Vegetables

Light penetration depth, as a characteristic parameter reflecting light attenuation and transmission in biological tissues, has been applied in nondestructive detection of fruits and vegetables. Recently, with emergence of new optical detection technologies, researchers have begun to explore methods evaluating optical properties of double-layer or even multilayer fruit and vegetable tissues due to the differences between peel and pulp in the chemical composition and physical properties, which has gradually promoted studies on light penetration depth. A series of demonstrated research on light penetration depth could ensure the accuracy of the optical information obtained from each layer of tissue, which is beneficial to enhance detection accuracy for quality assessment of fruits and vegetables. Therefore, the aim of this review is to give detailed outlines about the theory and principle of light penetration depth based on several emerging optical detection technologies and to focus primarily on its applications in the field of quality evaluation of fruits and vegetables, its future applicability in fruits and vegetables and the challenges it may face in the future.

High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis

Peanut kernels, known for their high nutritional value and palatability, are classified as nut food. In this study, peanut kernel samples from six distinct cities in Shandong Province, China, were examined to categorize and trace their origins. Near-infrared (NIR) spectra of samples were captured using a portable NIR-M-R2 spectrometer. After the application of Savitzky-Golay (SG) filtering, the classification was attempted using principal component analysis (PCA) plus linear discrimination analysis (LDA). Additionally, maximum uncertainty linear discriminant analysis (MLDA) was applied for comparison. A specific number of eigenvectors could respectively maximize the classification accuracies, 81.48% for PCA + LDA and 76.54% for MLDA. In order to further improve the classification accuracies, Adaboost-MLDA was proposed to develop a stronger classifier. This method, after 18 iterations, achieved remarkable effects, achieving a high accuracy of 95.06%. In a similar vein, the enhancement with preprocessing techniques multiplicative scatter correction (MSC) + SG and standard normal variate (SNV) + SG raised accuracies to 98.77% and 97.53%, respectively. The results of classifying first-order and second-order derivative spectra using Adaboost-MLDA were also described, achieving accuracies near 100%. The experiment demonstrates that integrating Adaboost with NIR spectroscopy offers a highly accurate method for peanut kernel classification, promising for practical applications in food quality control.

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.

Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy

Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky-Golay smoothing, SG; Savitzky-Golay 1 derivative, SG-1st; and Savitzky-Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R²p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R²p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.

Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network

Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.