A Clustering-Based Partial Least Squares Method for Improving the Freshness Prediction Model of Crucian Carps Fillets by Hyperspectral Image Technology

Quality Evaluation of Mackerel Fillets Stored under Different Conditions by Hyperspectral Imaging Analysis

This study was designed to compare the quality changes in mackerel fillets stored under different conditions by using hyperspectral imaging (HSI) techniques. Fillets packaged in vacuum were stored for six days under five different conditions: refrigerated at 4°C (R group); iced at 5±3°C (I group); kept at an ambient of 17±2°C (A group); frozen at -18°C for 24 h and thawed in a refrigerator at 4°C for 5 h on the sampling day (FTR group); FTR thawed in tap water instead of thawing in a refrigerator (FTW group). The FTR group had the lowest total bacterial count, drip loss, 2-thiobarbituric acid reactive substances, volatile basic nitrogen, and texture profile analysis values among groups during the entire storage period (p<0.05). Scanning electron microscopy revealed that the FTR group had less damage, while the other groups had shrunken muscle tissues. HSI integrated with the partial least squares model yielded reliable and efficient results, with high R2cv values, for several quality parameters of the mackerel fillets. Overall, the FTR group, involving freezing and thawing in a refrigerator, appears to be the most favorable option for maintaining the quality of mackerel fillets, which could be practically implemented in the industry. HSI is a suitable and effective technique for determining the quality of mackerel fillets stored under different conditions.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Variation in the terminology and methodologies applied to the analysis of water holding capacity in meat research

Studies examining meat quality variation, possibly resulting from animal physiology, processing, or ingredient additions, are likely to include at least one measure of water holding capacity (WHC). Methods for evaluating WHC can be classified as direct or indirect. Direct methods either gauge natural release of fluids from muscle or require the application of force to express water. The indirect methods do not actually measure WHC. They attempt to separate meat into two or three categories based on predictions of direct method results: the extreme of high and low WHC and an optional 'normal' group. Considerable statistical analyses are required to generate these predictive models. Presently, there are inconsistent terms (e.g., water holding, WHC, water binding, water binding potential/capacity) used to describe WHC and no standardized techniques recommended to evaluate it. To ensure that results can be compared across different laboratories, a better consensus must be reached in how these terms are employed and how this critical parameter is determined.