Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques

Meat quality evaluation based on computer vision technique: A review

Nowadays people tend to include more meat in their diet thanks to the improvement in standards of living as well as an increase in awareness of meat nutritive values. To ensure public health, therefore, there is a need for a rise in worldwide meat production and consumption. Further attention is also required as to how the safety and the quality of meat production process should be assessed. Classical methods of meat quality assessment, however, have some disadvantages; expensive and time-consuming. This study intends to introduce an alternative method known as Computer Vision (CV) for the assessment of various quality parameters of muscle foods. CV has several advantages over the traditional methods. It is non-destructive, easy, and quick, hence, more efficient in meat quality assessments. This study aims to investigate different quality characteristics of some muscle foods using CV. It closes with a discussion on the future challenges and expected opportunities of the practical application of CV in the meat industry.

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels

Raman spectra of human skin obtained by laser excitation have been used to non-invasively detect blood glucose. In previous reports, however, Raman spectra thus obtained were mainly derived from the epidermis and interstitial fluid as a result of the shallow penetration depth of lasers in skin. The physiological process by which glucose in microvessels penetrates into the interstitial fluid introduces a time delay, which inevitably introduces errors in transcutaneous measurements of blood glucose. We focused the laser directly on the microvessels in the superficial layer of the human nailfold, and acquired Raman spectra with multiple characteristic peaks of blood, which indicated that the spectra obtained predominantly originated from blood. Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively. The mean prediction performance of the 12 volunteers was obtained as an RMSEP of 0.45 mmol/L and R² of 0.95. It was no time lag between the predicted blood glucose and the actual blood glucose in the oral glucose tolerance test (OGTT). We also applied the procedure to data from all 12 volunteers regarded as one set, and the total predicted performance was obtained with an RMSEP of 0.27 mmol/L and an R² of 0.98, which is better than that of the individual model for each volunteer. This suggested that anatomical differences between volunteer fingernails do not reduce the prediction accuracy and 100% of the predicted glucose concentrations fall within Region A and B of the Clarke error grid, allowing acceptable predictions in a clinically relevant range. The Raman spectroscopy detection of blood glucose from microvessels is of great significance of non-invasive blood glucose detection of Raman spectroscopy. This innovative method may also facilitate non-invasive detection of other blood components.

Intelligent pH-sensitive indicator based on starch-cellulose and alizarin dye to track freshness of rainbow trout fillet

A novel pH-sensitive indicator was prepared by incorporation of alizarin in a starch-cellulose paper to monitor the freshness of rainbow trout fillet. Water solubility of the fabricated indicator reduced by incorporation of alizarin, while the percentage of swelling didn't change. FTIR certified that alizarin was properly incorporated which was revealed by the formation of intermolecular hydrogen bonding in the cellulose-starch matrix. SEM and ultraviolet-visible spectrum results also demonstrated that alizarin had good compatibility with the components of the indicator. The alizarin-starch-cellulose indicator (ASC) had greater color efficiency from yellow to purple at pH 2-11 and greater color stability after two months of storage at 4 °C. It can be concluded that the color changes of ASC, from orange to reddish brown, were entirely corresponded with TVB-N contents of fish, which presents a satisfactory capability of developed indicator to identify the initiation of spoilage in the refrigerated fish fillet.

Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose

The aim of this study was to use an electronic nose set up in our lab to detect and predict the freshness of pork, beef and mutton. Three kinds of freshness, including fresh, sub-fresh and putrid, was established by human sensory evaluation and was used as a reference for the electronic nose’s discriminant factor analysis. The principal component analysis results showed the electronic nose could distinguish well pork, beef and mutton samples with different storage times. In the PCA figures, three kinds of meats samples all presented an approximate parabola trend during 7 days’ storage time. The discriminant factor analysis showed electronic nose could distinguish and judge well the freshness of samples (accuracy was 89.5%, 84.2% and 94.7% for pork, beef and mutton, respectively). Therefore, the electronic nose is promising for meat fresh detection application.

Synthesized Au NPs@silica composite as surface-enhanced Raman spectroscopy (SERS) substrate for fast sensing trace contaminant in milk

With increased concerns on milk safety issues, the development of a simple and sensitive method to detect 2,4-dichlorophenoxyacetic acid (2,4-D), a common contaminant in milk, becomes relevant in safeguarding human health threats that results from its consumption. Surface-enhanced Raman spectroscopy (SERS) shows excellent ability for various targets analysis but its usage for rapid and accurate determination of analyte via SERS presents challenges. This study attempted the quantification of 2,4-dichlorophenoxyacetic acid (2,4-D) residue in milk using a novel SERS active substrate- decorated silica films with Au nanoparticles (Au NPs@ silica) coupled to chemometric algorithms. Au NPs@ silica composite was synthesized as a SERS sensor through self-assembly. Thereafter, the SERS spectrum of 2,4-D extract from milk with different concentrations based on the developed SERS sensor was collected and the spectra were analyzed by partial least squares (PLS), and variable selection algorithms - genetic algorithm-PLS (GA-PLS), competitive-adaptive reweighted sampling-PLS (CARS-PLS) and ant colony optimization-PLS (ACO-PLS), to develop quantitative models for 2,4-D prediction. The results obtained showed that the CARS-PLS model gave the optimum result with LOD of 0.01 ng/mL realized and a determination coefficient in the prediction set of (R_P) = 0.9836 within a linear range of 10^-2 to 10⁶ ng/mL was achieved. Au NPs@ silica SERS sensor combined with CARS-PLS may be employed for rapid quantification of 2,4-D extract from milk towards its quality and safety monitoring.

Changes in the microbial communities in vacuum-packaged smoked bacon during storage

This study aimed to gain deeper insights into the microbiota composition and population dynamics, monitor the dominant bacterial populations and identify the specific spoilage microorganisms (SSOs) of vacuum-packed bacon during refrigerated storage using both culture-independent and dependent methods. High-throughout sequencing (HTS) showed that the microbial composition changed greatly with the prolongation of storage time. The diversity of microbiota was abundant at the initial stage then experienced a continuous decrease. Lactic acid bacteria (LAB) mainly Leuconostoc and Lactobacillus dominated the microbial population after seven days of storage. A total of 26 isolates were identified from different growth media using traditional cultivation isolation and identification method. Leuconostoc mesenteroides and Leuconostoc carnosum were the most prevalent species since day 15, while Lactobacillus sakei and Lactobacillus curvatus were only found on day 45, suggesting that they could be responsible for the spoilage of bacon. Serratia, Rahnella, Fusobacterium and Lactococcus underwent a dramatic increase at some point in individual batchs which may be considered as potential contributors to the spoilage.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools

Tea is one of the three greatest beverages in the world. In China, green tea has the largest consumption, and needle-shaped green tea, such as Maofeng tea and Sparrow Tongue tea, accounts for more than 40% of green tea (Zhu et al., 2017). The appearance of green tea is one of the important indexes during the evaluation of green tea quality. Especially in market transactions, the price of tea is usually determined by its appearance (Zhou et al., 2012). Human sensory evaluation is usually conducted by experts, and is also easily affected by various factors such as light, experience, psychological and visual factors. In the meantime, people may distinguish the slight differences between similar colors or textures, but the specific levels of the tea are hard to determine (Chen et al., 2008). As human description of color and texture is qualitative, it is hard to evaluate the sensory quality accurately, in a standard manner, and objectively. Color is an important visual property of a computer image (Xie et al., 2014; Khulal et al., 2016); texture is a visual performance of image grayscale and color changing with spatial positions, which can be used to describe the roughness and directivity of the surface of an object (Sanaeifar et al., 2016). There are already researchers who have used computer visual image technologies to identify the varieties, levels, and origins of tea (Chen et al., 2008; Xie et al., 2014; Zhu et al., 2017). Most of their research targets are crush, tear, and curl (CTC) red (green) broken tea, curly green tea (Bilochun tea), and flat-typed green tea (West Lake Dragon-well green tea) as the information sources. However, the target of the above research is to establish a qualitative evaluation method on tea quality (Fu et al., 2013). There is little literature on the sensory evaluation of the appearance quality of needle-shaped green tea, especially research on a quantitative evaluation model (Zhou et al., 2012; Zhu et al., 2017).

Influences of cold atmospheric plasma on microbial safety, physicochemical and sensorial qualities of meat products

Meat and meat products can be contaminated with pathogenic microorganisms, which cause serious health problems and economic loss. Recently, numerous novel non-thermal technologies have been developed to respond to growing consumer demand for high quality and safe meat products. Cold atmospheric plasma (CAP) is a novel and emerging non-thermal technology, showing great potential for applications in the food industry. This review presents recent advances on the developments and applications of CAP in meat products, including generation and microbial inactivation effects of CAP as well as its influences on physicochemical qualities and sensory attributes of meat products. Furthermore, the safety assessment of CAP-treated meat products and challenges in industrial application of CAP are also discussed.

Lactobacillus plantarum (KACC 92189) as a Potential Probiotic Starter Culture for Quality Improvement of Fermented Sausages

This study was conducted to evaluate the effects of fermenting temperature on the applicability of Lactobacillusplantarum for production of fermented sausages as starter cultures, and its applicable efficiency was also compared with those inoculated with commercial starter culture or non-inoculated control. The L. plantarum isolated from a naturally-fermented meat, identified by 16S rDNA sequencing and again identified by denovo Assembly Analysis method was used as a starter culture. Six treatments: 3 with L. plantarum at different fermenting temperatures (20, 25 and 30°C), and other 3 treatments (1 with commercial starter culture, 1 with its mixture with L. plantarum and 1 non-inoculated control) fermented under the same conditions (25°C) were prepared. Results revealed that the fermenting temperature considerably affected the pH change in samples added with L. plantarum; the highest pH drop rate (1.57 unit) was obtained on the samples fermented at 30°C, followed by those at 25°C (1.3 unit) and 20°C (0.99 unit) after 4 days fermentation. Increasing the temperature up to 30°C resulted in significantly lower spoilage bacteria count (5.15 log CFU/g) and lipid oxidation level in the products inoculated with L. plantarum. The sensory analysis also showed that the samples added with L. plantarum at 30°C had significantly higher odor, taste and acceptability scores than those fermented at lower temperatures. Under the same processing condition, although the L. plantarum showed slightly lower acidification than the commercial starter culture, however, it significantly improved the eating quality of the product.

Application of Electronic Nose Systems on Animal-Source Food

Electronic nose, which is designed to perceive artificially the odour-active molecules in a sample headspace, has seen an increased use in the food industry as a rapid and reliable tool for quality assessment, classification, and authentication of several food items. The use of chemometrics and pattern recognition methods, together with gas sensors, emerged to be a very powerful analytical approach. In this chapter, an overview of the recent achievements in the field of electronic nose applications on animal-source food is given. Moreover, the authors deal with the recent research trends to overcome the actual sensor shortcomings, including sensor fusion techniques and their applications to evaluate animal-source foods and novel electronic nose systems.

Bio-protective potential of lactic acid bacteria: Effect of Lactobacillus sakei and Lactobacillus curvatus on changes of the microbial community in vacuum-packaged chilled beef

OBJECTIVE

This study was to determine the bacterial diversity and monitor the community dynamic changes during storage of vacuum-packaged sliced raw beef as affected by Lactobacillus sakei and Lactobacillus curvatus.

METHODS

L. sakei and L. curvatus were separately incubated in vacuumed-packaged raw beef as bio-protective cultures to inhibit the naturally contaminating microbial load. Dynamic changes of the microbial diversity of inoculated or non-inoculated (control) samples were monitored at 4°C for 0 to 38 days, using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE).

RESULTS

The DGGE profiles of DNA directly extracted from non-inoculated control samples highlighted the order of appearance of spoilage bacteria during storage, showing that Enterbacteriaceae and Pseudomonas fragi emerged early, then Brochothrix thermosphacta shared the dominant position, and finally, Pseudomonas putida showed up became predominant. Compared with control, the inoculation of either L. sakei or L. curvatus significantly lowered the complexity of microbial diversity and inhibited the growth of spoilage bacteria (p<0.05). Interestingly, we also found that the dominant position of L. curvatus was replaced by indigenous L. sakei after 13 d for L. curvatus-inoculated samples. Plate counts on selective agars further showed that inoculation with L. sakei or L. curvatus obviously reduced the viable counts of Enterbacteraceae, Pseudomonas spp. and B. thermosphacta during later storage (p< 0.05), with L. sakei exerting greater inhibitory effect. Inoculation with both bio-protective cultures also significantly decreased the total volatile basic nitrogen values of stored samples (p<0.05).

CONCLUSION

Taken together, the results proved the benefits of inoculation with lactic acid bacteria especially L. sakei as a potential way to inhibit growth of spoilage-related bacteria and improve the shelf life of vacuum-packaged raw beef.

Assessment of frozen storage duration effect on quality characteristics of various horse muscles

Objective

The study aimed at assessing the effects of frozen storage duration on quality characteristics, lipid oxidation and sensory quality of various horse muscles.

Methods

Five representative muscles: longissimus dorsi (LD), gluteus medius (GM), semimembranosus (SM), biceps femoris (BF), and triceps brachii (TB) at 24 h post-mortem obtained from 28-mo-old Jeju female breed horses (n = 8) were used in the present investigation. The muscles were vacuum-packaged and frozen at −20°C for 120, 240, and 360 days. All the samples were analyzed for thawing and cooking losses, pH, Warner–Bratzler shear forces (WBSF), color traits, total volatile basic nitrogen (TVBN), thiobarbituric acid reactive substances (TBARS) and sensory traits. The muscle samples analyzed on day 0 of frozen storage (fresh, non-frozen) were used for comparison.

Results

Results revealed that thawing and cooking losses significantly (p<0.05) increased in all the muscles after 120 days and then remained unchanged up to 360 days of frozen storage. The TBARS and TVBN contents significantly increased as increasing frozen storage time up to 360 days (p<0.05). While, significant decreases in WBSF values were observed for all the muscles with increased frozen storage time (p<0.05). Frozen storage variously affected the color traits of the muscles for instance; the redness of LD, GM, and BF muscles showed a decreasing tendency during frozen storage while it was not changed in TB and SM muscles. Furthermore, the frozen storage did not produce detrimental effects on sensory quality as it did not cause flavor and juiciness defects whereas it partially improved the tenderness of all the muscles studied.

Conclusion

Based on the results obtained from our work, it is concluded that frozen storage could be applied to increase the long-term shelf life of horsemeat while still retaining its sensory quality.

Electronic noses: Powerful tools in meat quality assessment

Main factors that are considered by consumers when choosing meat products are colour and aroma, of which the latter is a more reliable indicator of quality. However, a simple sensory evaluation of hedonistic qualities is often not sufficient to determine whether protein is past its shelf life, and consumption of spoiled meat can lead to serious health hazards. Some volatile compounds can be used as spoilage indicators, and so a device equipped with a sensor sensitive to particular odorants would prove useful. Unfortunately, no such single compound has yet been identified, as the changes taking place in a sample of meat during storage are contingent on numerous factors. On the other hand, a combination of volatile compounds may form a unique 'fingerprint' which can be analysed pattern recognition algorithms with an electronic nose. It can supplement established techniques of meat quality assessment by providing results that correlate well with hedonic perception in a short time and at a low cost.

A novel method for the discrimination of Hawthorn and its processed products using an intelligent sensory system and artificial neural networks

Hawthorn (CFS) has commonly been applied as an important traditional Chinese medicine and food for thousands of years. The raw material of CFS is commonly processed by stir-frying to obtain yellow (CFY), dark brown (CFD), and carbon dark (CFC) colored products, which are used for different clinical uses. In this study, an intelligent sensory system (ISS) was used to obtain the color, gas, and flavor samples data, which were further employed to develop a novel and accurate method for the identification of CFS and its processed products using principal component analysis. Moreover, this research developed a model of an artificial neural network, which could be used to predict the total organic acid, total flavonoids, citric acid, hyperin, and 5-hydroxymethyl furfural via determination of the color, odor, and taste of a sample. In conclusion, the ISS and the artificial neural network are useful tools for rapid, accurate, and effective discrimination of CFS and its processed products.