In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor

Classification of soybean chemical characteristics by excitation emission matrix coupled with t-SNE dimensionality reduction

Measuring the chemical composition in soybeans is time-consuming and laborious, and even simple near-infrared sensors generally require the creation of calibration curves before application. In this study, a new screening method for soybeans without calibration curves was investigated by combining the excitation emission matrix (EEM) and dimensionality reduction analysis. The EEMs of 34 soybean samples were measured, and representative chemical contents including crude protein, crude oil and isoflavone contents were measured by chemical analysis. Two methods of dimensionality reduction: principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) were applied on the EEM data to obtain two-dimensional plots, which were divided into two regions with large or small amount of each chemical components. To classify the large or small levels of each of the chemical composition, machine learning classification models were constructed on the two-dimensional plots after dimensionality reduction. As a result, the classification accuracy was higher in t-SNE than in the combinations of PC1 and PC2 from PCA. Furthermore, in t-SNE, the classification accuracy reached over 90% for all the chemical components. From these results, t-SNE dimensionality reduction on the soybean EEM has the potential for easy and accurate screening of soybeans especially based on isoflavone contents.

Comparative physiological, biochemical and transcriptomic analyses to reveal potential regulatory mechanisms in response to starvation stress in Cipangopaludina chinensis

Cipangopaludina chinensis, as a financially significant species in China, represents a gastropod in nature which frequently encounters starvation stress owing to its limited prey options. However, the underlying response mechanisms to combat starvation have not been investigated in depth. We collected C. chinensis under several times of starvation stress (0, 7, 30, and 60 days) for nutrient, biochemical characteristics and transcriptome analyses. The results showed that prolonged starvation stress (> 30 days) caused obvious fluctuations in the nutrient composition of snails, with dramatic reductions in body weight, survival and digestive enzyme activity (amylase, protease, and lipase), and markedly enhanced the antioxidant enzyme activities of the snails. Comparative transcriptome analyses revealed 3538 differentially expressed genes (DEGs), which were significantly associated with specific starvation stress-responsive pathways, including oxidative phosphorylation and alanine, aspartate, and glutamate metabolism. Then, we identified 40 candidate genes (e.g., HACD2, Cp1, CYP1A2, and GPX1) response to starvation stress through STEM and WGCNA analyses. RT-qPCR verified the accuracy and reliability of the high-throughput sequencing results. This study provides insights into snail overwintering survival and the potential regulatory mechanisms of snail adaptation to starvation stress.

Discrimination of male-sterility and male-fertility in Japanese cedar (Cryptomeria japonica) using near-infrared diffuse transmission spectroscopy

The increasing demand for pollen-free seedlings of Japanese cedar (Cryptomeria japonica) has created a need for a simple method to discriminate between male-sterile and male-fertile strobili. The objective of this study was to establish a classification model to quickly and easily distinguish male-sterile and male-fertile strobili in C. japonica using near-infrared (NIR) diffuse transmission spectroscopy. The absorbance spectra of C. japonica were obtained for three different months from December 2022 to February 2023 and preprocessed using three methods: untreated, smoothing, and second derivative. Principal component analysis was applied to the NIR spectra and classification models were built using a support vector machine. The sample collected in January 2023 showed the highest discrimination accuracy of 89.38% with the smoothing preprocessing, which was improved to 89.97% by limiting the wavelengths to the NIR region. Furthermore, discrimination accuracy for independent test data was evaluated by splitting the data into training and testing sets using January 2023 data with smoothing preprocessing. The discrimination accuracy for test data sets was more than 85%, and the misclassification ratio was less than 20% for each sample group. These results indicate the potential of using NIR diffuse transmission spectroscopy to discriminate between male-sterility and fertility in C. japonica.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Elemental sulfur concentration can be used as a rapid, reliable, and cost-effective predictor of sulfur amino acid content of soybean seeds

In this study, we have examined the feasibility of using elemental sulfur content of soybean seeds as a proxy for the overall sulfur amino acid content of soybean seeds. Earlier, we have identified by high throughput ionomic phenotyping several high and low sulfur containing soybean lines from the USDA Soybean Germplasm Collection. Here, we measured the cysteine and methionine content of select soybean lines by high-performance liquid chromatography. Our results demonstrate that those soybean lines which had high elemental sulfur content also had a higher cysteine and methionine content when compared to soybean lines with low elemental sulfur. SDS-PAGE and immunoblot analysis revealed that the accumulation of Bowman Birk protease inhibitor and lunasin in soybean seeds may only be marginally correlated with the elemental sulfur levels. However, we found a positive correlation between the levels of trypsin and chymotrypsin inhibitor activities and elemental sulfur and sulfur amino acid content of the seeds. Thus, elemental sulfur content and/or protease inhibitor activity measurement can be utilized as a rapid and cost-effective method to predict the overall sulfur amino acid content of soybean seeds. Our findings will benefit breeders in their endeavors to develop soybean cultivars with enhanced sulfur amino acid content.

Phloem exudate metabolic content reflects the response to water-deficit stress in pea plants (Pisum sativum L.)

Drought stress impacts the quality and yield of Pisum sativum. Here, we show how short periods of limited water availability during the vegetative stage of pea alters phloem sap content and how these changes are connected to strategies used by plants to cope with water deficit. We have investigated the metabolic content of phloem sap exudates and explored how this reflects P. sativum physiological and developmental responses to drought. Our data show that drought is accompanied by phloem-mediated redirection of the components that are necessary for cellular respiration and the proper maintenance of carbon/nitrogen balance during stress. The metabolic content of phloem sap reveals a shift from anabolic to catabolic processes as well as the developmental plasticity of P. sativum plants subjected to drought. Our study underlines the importance of phloem-mediated transport for plant adaptation to unfavourable environmental conditions. We also show that phloem exudate analysis can be used as a useful proxy to study stress responses in plants. We propose that the decrease in oleic acid content within phloem sap could be considered as a potential marker of early signalling events mediating drought response.

Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis

The objective of this study was to develop a rapid technique to authenticate potato chip frying oils using vibrational spectroscopy signatures in combination with pattern recognition analysis. Potato chip samples (n = 118) were collected from local grocery stores, and the oil was extracted by a hydraulic press and characterized by fatty acid profile determined by gas chromatography equipped with a flame ionization detector (GC-FID). Spectral data was collected by a handheld Raman system (1064 nm) and a miniature near-infrared (NIR) sensor, further being analyzed by SIMCA (Soft Independent Model of Class Analogies) and PLSR (Partial Least Square Regression) to develop classification algorithms and predict the fatty acid profile. Supervised classification by SIMCA predicted the samples with a 100% sensitivity based on the validation data. The PLSR showed a strong correlation (Rval > 0.97) and a low standard error of prediction (SEP = 1.08-3.55%) for palmitic acid, oleic acid, and linoleic acid. 11% of potato chips (n = 13) indicated a single oil in the label with a mislabeling problem. Our data supported that the new generation of portable vibrational spectroscopy devices provided an effective tool for rapid in-situ identification of oil type of potato chips in the market and for surveillance of accurate labeling of the products.