A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging

Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review

Food safety is always of paramount importance globally due to the devasting social and economic effects of foodborne disease outbreaks. There is a high consumption rate of meat worldwide, making it an essential protein source in the human diet, hence its microbial safety is of great importance. The food industry stakeholders are always in search of methods that ensure safe food whilst maintaining food quality and excellent sensory attributes. Currently, there are several methods used in microbial food analysis, however, these methods are often time-consuming and do not allow real-time analysis. Considering the recent technological breakthroughs in artificial intelligence and machine learning, it raises the question of whether these advancements could be leveraged within the meat industry to improve turnaround time for microbial assessments. Hyperspectral imaging (HSI) is a highly prospective technology worth exploring for microbial analysis. The rapid, non-destructive method has the potential to be integrated into food production systems and allows foodborne pathogen detection in food samples, thus saving time. Although there has been a substantial increase in research on the utilisation of HSI in food applications over the past years, its use in the microbial assessment of meat is not yet optimal. This review aims to provide a basic understanding of the visible-near infrared HSI system, recent applications in the microbial assessment of meat products, challenges, and possible future applications.

Isotachophoretic quantification of total viable bacteria on meat and surfaces

BACKGROUND

The quantification of microbes, particularly live bacteria, is of utmost importance in assessing the quality of meat products. In the context of meat processing facilities, prompt identification and removal of contaminated carcasses or surfaces is crucial to ensuring the continuous production of safe meat for human consumption. The plate count method and other traditional detection methods are not only labour-intensive but also time-consuming taking 24-48 h.

RESULTS

In this report, we present a novel isotachophoretic quantification method utilizing two nucleic acid stains, SYTO9 and propionic iodide, for the detection of total viable bacteria. The study employed E. coli M23 bacteria as a model organism, with an analysis time of only 30 min. The method demonstrated a limit of detection (LOD) of 184 CFU mL-1 and 14 cells mL-1 for total viable count and total cell count, respectively. Furthermore, this new approach is capable of detecting the microbial quality standard limits for food contacting surfaces (10 CFU cm-2) and meat (1.99 × 104 CFU cm-2) by swabbing an area of 10 × 10 cm2.

SIGNIFICANCE

In contrast to the culture-based methods usually employed in food processing facilities, this isotachophoretic technique enables easy and rapid detection (<30 min) of microorganisms, facilitating crucial decision-making essential for maintaining product quality and safety.

An FPGA-Based Machine Learning Tool for In-Situ Food Quality Tracking Using Sensor Fusion

The continuous development of more accurate and selective bio- and chemo-sensors has led to a growing use of sensor arrays in different fields, such as health monitoring, cell culture analysis, bio-signals processing, or food quality tracking. The analysis and information extraction from the amount of data provided by these sensor arrays is possible based on Machine Learning techniques applied to sensor fusion. However, most of these computing solutions are implemented on costly and bulky computers, limiting its use in in-situ scenarios outside complex laboratory facilities. This work presents the application of machine learning techniques in food quality assessment using a single Field Programmable Gate Array (FPGA) chip. The characteristics of low-cost, low power consumption as well as low-size allow the application of the proposed solution even in space constrained places, as in food manufacturing chains. As an example, the proposed system is tested on an e-nose developed for beef classification and microbial population prediction.

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Anthocyanin Colorimetric Strip for Volatile Amine Determination

Food freshness is one of the main concerns of consumers. Food spoilage is mainly caused by contamination and microbial growth in which the latter produces volatile amines in the process. Several methods have been used to determine volatile amines to indicate food freshness, and indicator films are deemed as the most time-efficient and economical. In this study, anthocyanin was extracted from mangosteen rind as a natural dye indicator and was incorporated in a chitosan/PVA polymer matrix. The film with different concentrations of anthocyanin extract (5%, 15%, and 25%) was prepared and tested for their sensitivity to 136 ppm ammonia vapor followed by colorimetric analysis using ImageJ software. The film with 25% anthocyanin yielded the most visible color change upon exposure to ammonia vapor. The color changed from pink to yellowish-brown within 14 minutes of exposure. The RGB-converted images of the film with 25% anthocyanin extract showed gradual loss of red coloration being replaced by cyan spots. FTIR spectra showed incorporation of anthocyanin to the chitosan/PVA matrix with the decrease in the intensity of the C-N stretching peak. Thermogravimetric analysis showed that the film has high thermal stability with onset temperature of 310.43°C. Thus, the film developed is an excellent candidate for optimization and production of a thermally stable amine detector for food products.

Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications

Hyperspectral imaging (HSI) is a technology integrating optical sensing technologies of imaging, spectroscopy, and chemometrics. The sensor of HSI can obtain both spatial and spectral information simultaneously. Therefore, the chemical and physical information of food products can be monitored in a rapid, nondestructive, and noncontact manner. There are numerous reports and papers and much research dealing with the applications of HSI in food in recent years. This review introduces the principle of HSI technology, summarizes its recent applications in food, and pinpoints future trends.

Advanced Analysis of Roots and Tubers by Hyperspectral Techniques

Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.

Predicting Pork Color Scores Using Computer Vision and Support Vector Machine Technology

The objective of this study was to investigate the ability of image color features to predict subjective pork color scores. Subjective and instrumental color were assessed on the bloomed, cross-sectional surface of pork longissimus thoracis et lumborum chops. Images of pork chop samples were acquired using a computer vision system, and 18 image color features (mean and standard deviation of R, G, B, H, S, I, L*, a*, b*) were extracted for inclusion in partial least squares (PLS) and support vector machine (SVM) regression models. For color scores 2, 3, 4, and 5, the accuracies were 50.4, 75.9, 72.4, and 47.3% classified correctly by PLS, respectively, and 70.7, 72.8, 76.7, and 69.7% by SVM, respectively. The overall prediction accuracies of 2 models for pork color scores were 68.3% for PLS and 73.4% for SVM. There was minimal major misclassification of samples (< 0.5%). Image color features isolated through the development of PLS and SVM models, particularly SVM, show potential as a method to predict pork color scores.

Application of biopolymers for improving the glass transition temperature of hairtail fish meat

BACKGROUND

Glass transition temperature (T_g ) and food moisture content are closely related, especially in foods with a high moisture content, such as surimi products. In order to improve storage condition and maintain food quality, the influence of six biopolymers on the T_g of hairtail fish meat paste was investigated by differential scanning colorimetry.

RESULTS

Samples were stored at -8 °C (>T_g ), -14 °C (T_g ) and -18 °C (<T_g ) for 105 days. The results showed that trehalose, maltodextrin and xanthan could obviously improve the T_g of the hairtail fish meat paste. Trehalose at 4.1% (w/w), maltodextrin at 5.0% and xanthan at 0.41% were determined as the optimum amounts, with the predicted value of T_g to be -14.1 °C. The total viable counts (TVC) and total volatile basic nitrogen (TVB-N) of samples stored at -14 and -18 °C were much lower and the gel strength and whiteness maintained much better than those of -8 °C.

CONCLUSION

Addition of biopolymers could effectively restrain the increase in TVC and TVB-N, as well as the dropping of gel strength and whiteness of fish meat paste during storage. A mixture of the biopolymers could improve T_g and showed great potential in the preservation of hairtail meat products. © 2017 Society of Chemical Industry.

Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique

Quality determination of frozen food is a time-consuming and laborious work as it normally takes a long time to thaw the frozen samples before measurements can be carried out. In this research, a rapid and non-destructive determination technique for frozen pork quality was tested with a hyperspectral imaging (HSI) system. In this study, 120 pieces of pork meat were frozen by four kinds of methods with various freezing temperatures from -20 to -120°C. The hyperspectral images of the samples were acquired at the frozen state. Quality indicators including drip loss, pH value, color, cooking loss and Warner-Bratzler shear force (WBSF) of the samples were measured after thawing. The spectral characteristics of the frozen meat samples were studied and it was revealed that the reflectance at 1100nm had a close relationship with the freezing temperature (R=-0.832, p<0.01). Partial least squares regression (PLSR) was applied to establish the spectral models, and the models were then optimized. Results showed that the improved region of interest (ROI) method could be used to extract effective spectral information to withstand the interference of freezing, and choosing appropriate spectral bands and spectral pretreatment techniques were crucial to develop robust mathematical model. The performances of the models established were diverse based on different quality indicators. The coefficients of determination for prediction (Rp(2)) for L*, cooking loss, b*, drip loss and a* were 0.907, 0.845, 0.814, 0.762, and 0.716, respectively. However there were low correlations (Rp(2)) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing.