Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging.

This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000-1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging.

Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness.

Metabolomic understanding of pod removal effect in soybean plants and potential association with their health benefit.

Since natural materials, such as phytochemicals in plants, are increasingly being used for foods and skincare due to their beneficial functions, it is important for developing the cultivation practices to increase the contents of phytochemicals. We here explored metabolite perturbations in the leaves of soybean plants when their pods were removed during growth through 1H NMR-based metabolomics approach. There were obvious metabolic differences in the leaves between normal and pod-removed soybean plants. High amounts of primary metabolites in pod-removed soybean leaves, including amino acids, sugars, and fatty acids, reflected a delay of leaf senescence caused by pod removal. In particular, amounts of isoflavones, coumestrol, and apigenin derivatives in pod-removed soybean leaves were substantially increased. These were considered as distinct metabolic influences of pod removal in soybean plants. These results indicate that pod removal of soybean plants can induce significant perturbations of various metabolites in their soybean leaves, providing useful information to improve the quality of soybean leaves by increasing amounts of bioactive components.

Metabotyping of different soybean genotypes and distinct metabolism in their seeds and leaves.

The metabolome of three soybean genotypes, Glycine max Hwangkeum (elite or domesticated cultivar), Glycine max Napjakong (landrace or semi-wild cultivar) and Glycine soja Dolkong (wild cultivar), were characterized in seeds and leaves using a 1H NMR-based metabolomics approach. Expression of primary and secondary metabolites were different in seeds and leaves as well as amongst soybean genotypes. Different kaempferol glycosides were observed in the leaves but not in the seeds, and quercetin derivatives were found only in G. max Napjakong and G. soja Dolkong. Moreover, epicatechin was found only in the seeds of G. max Napjakong and G. soja Dolkong. These results demonstrate distinct adaptations of different soybean genotypes to given environmental conditions. The current study, therefore, provides useful information on global metabolic compositions that might be used to develop soybean-based products through better understanding of the metabolic phenotypes of existing soybean genotypes.

Metabolomics approach for understanding geographical dependence of soybean leaf metabolome.

The soybean plant (Glycine max) is widely used as an ingredient in various foods, nutraceuticals and cosmetics, due to their diverse bioactive compounds. Their metabolic compositions are likely affected by environmental conditions during growth. To investigate the influence of different environmental conditions on the metabolite composition of soybean leaves, we cultivated soybean (G. max Sinhwa) in the southernmost island and volcanic region of Korea, and in the central section and limestone region of the Korean peninsula. Comprehensive metabolite variations of their leaves were analyzed through 1H NMR-based metabolomics approach. With marked differences in soil compositions and climatic conditions between the two growing areas, differences in accumulations of pinitol and diverse flavonoids were noted between the soybean leaves, reflecting the distinct metabolism of soybean plants for physiological adaptation toward different environmental conditions. Therefore, the current study highlights the geographical dependences of diverse soybean leaf metabolites for developing biofunction-enhanced soybean products.

Distinctive Metabolism of Flavonoid between Cultivated and Semiwild Soybean Unveiled through Metabolomics Approach.

Soybeans are an important crop for agriculture and food, resulting in an increase in the range of its application. Recently, soybean leaves have been used not only for food products but also in the beauty industry. To provide useful and global metabolite information on the development of soy-based products, we investigated the metabolic evolution and cultivar-dependent metabolite variation in the leaves of cultivated (Glycine max) and semiwild (G. gracilis) soybean, through a (1)H NMR-based metabolomics approach, as they grew from V (vegetative) 1 to R (reproductive) 7 growth stages. The levels of primary metabolites, such as sucrose, amino acids, organic acids, and fatty acids, were decreased both in the G. gracilis and G. max leaves. However, the secondary metabolites, such as pinitol, rutin, and polyphenols, were increased while synthesis of glucose was elevated as the leaves grew. When metabolite variations between G. gracilis and G. max are compared, it was noteworthy that rutin and its precursor, quercetin-3-O-glucoside, were found only in G. gracilis but not in G. max. Furthermore, levels of pinitol, proline, ß-alanine, and acetic acid, a metabolite related to adaptation toward environmental stress, were different between the two soybean cultivars. These results highlight their distinct metabolism for adaptation to environmental conditions and their intrinsic metabolic phenotype. This study therefore provides important information on the cultivar-dependent metabolites of soybean leaves for better understanding of plant physiology toward the development of soy-based products.