Metabolomics combined with chemometric tools (PCA, HCA, PLS-DA and SVM) for screening cassava (Manihot esculenta Crantz) roots during postharvest physiological deterioration

Biochemical characterisation of a cassava (Manihot esculenta crantz) diversity panel for post-harvest physiological deterioration; metabolite involvement and environmental influence

Cassava (Manihot esculenta Crantz) produces edible roots, a major carbohydrate source feeding more than 800 million people in Africa, Latin America, Oceania and Asia. Post-harvest physiological deterioration (PPD) renders harvested cassava roots unpalatable and unmarketable. Decades of research on PPD have elucidated several genetic, enzymatic and metabolic processes involved. Breeding populations were established to enable verification of robust biomarkers for PPD resistance. For comparison, these PPD populations have been cultivated concurrently with diversity population for carotenoid (β-carotene) content. Results highlighted a significant variation of the chemotypes due to environmental factors. Less than 3% of the detected molecular features showed consistent trends between the two harvest years and were putatively identified as phenylpropanoid derived compounds (e.g. caffeoyl rutinoside). The data corroborated that ∼20 μg β-carotene/g DW can reduced the PPD response of the cassava roots to a score of ∼1. Correlation analysis showed a significant correlation of β-carotene content at harvest to PPD response (R2 -0.55). However, the decrease of β-carotene over storage was not significantly correlated to initial content or PPD response. Volatile analysis observed changes of apocarotenoids derived from β-carotene, lipid oxidation products (alkanes, alcohols and carbonyls and esters) and terpenes. The majority of these volatiles (>90%) showed no significant correlation to β-carotene or PPD. Observed data indicated an increase (∼2-fold) of alkanes in varieties with β-carotene >10 μg/g DW and a decrease (∼60%) in varieties with less β-carotene. Fatty acid methyl esters with a chain length > C9 were detected solely after storage and show lower levels in varieties with higher β-carotene content. In combination with correlation values to PPD (R2 ∼0.3; P-value >0.05), the data indicated a more efficient ROS quenching mechanism in PPD resistant varieties.

Geographical authentication of saffron by chemometrics applied to the ion mobility spectrometry data

There is a lack of a reliable tool for quickly determining the geographical origins of saffron (SFR). Ion mobility spectrometry (IMS) has emerged as a promising method for rapid authentication. In this study, 232 Iranian SFR samples harvested in five distinct areas (Khorasan, Azerbaijan, Golestan, Fars, and Isfahan) were analyzed by IMS coupled with chemometric methods. The principal component analysis (PCA) was applied for analyzing the collected IMS data, utilizing three principle components (PCs) that accounted for 81 % of the explained variance. Moreover, the partial least squares-discriminant analysis (PLS-DA) demonstrated the average sensitivity and specificity rates, of 72.3 % to 92.5 % for the exernal test set and 75.5 % to 94.3 % for training set. The accuracy values were ≥ 85.0 % for the prediction set for all classes of samples. The results of this study revealed a successful application of IMS and chemometric methods for rapid geographical authentication of saffron samples in Iran.

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

LC-HRMS Profiling and Cytotoxic Potential of Actinomycetes Associated with the Red Sea Soft Coral Sarcophyton glaucum: In vitro and In silico Studies

In the current study, the actinomycetes associated with the red sea-derived soft coral Sarcophyton glaucum were investigated in terms of biological and chemical diversity. Four different media, M1, ISP2, Marine Agar (MA), and Actinomycete isolation agar (AIA) were used for the isolation of three strains of actinomycetes that were identified as Streptomyces sp. UR 25, Micromonospora sp. UR32 and Saccharomonospora sp. UR 19. LC-HRMS analysis was used to investigate the chemical diversity of the isolated actinobacteria. The LC-HRMS data were statistically processed using MetaboAnalyst 5.0 viz to differentiate the extract groups and determine the optimal growth culturing conditions. Multivariate data statistical analysis revealed that the Micromonospora sp. extract cultured on (MA) medium is the most distinctive extract in terms of chemical composition. While, the Streptomyces sp. UR 25 extracts are differ significantly from Micromonospora sp. UR32 and Saccharomonospora sp. UR 19. Biological investigation using in vitro cytotoxic assay for actinobacteria extracts revealed the prominent potentiality of the Streptomyces sp. UR 25 cultured on oligotrophic medium against human hepatoma (HepG2), human breast adenocarcinoma (MCF-7) and human colon adenocarcinoma (CACO2) cell lines (IC50 =3.3, 4.2 and 6.8 μg/mL, respectively). SwissTarget Prediction speculated that among the identified compounds, 16-deethyl, indanomycin (8) could have reasonable affinity on HDM2 active site. In this respect, molecular docking study was performed for compound (8) to reveal a substantial affinity on HDM2 active site. In addition, molecular dynamics simulations were carried out at 200 ns for the most active compound (8) compared to the co-crystallized inhibitor DIZ giving deeper information regarding their thermodynamic and dynamic properties as well.

Spraying chitosan on cassava roots reduces postharvest deterioration by promoting wound healing and inducing disease resistance

Postharvest damage makes cassava roots vulnerable to pathogen infections and decay, which significantly hinders the development of the cassava industry. The objective of this study was to assess the antibacterial properties of chitosan in vitro, as well as its effect on wound healing and resistance in cassava roots. The findings demonstrated that the bacteriostatic effect of chitosan became increasingly prominent as the concentration of chitosan enhanced. Chitosan at a concentration of 0.5 mg/mL was revealed to significantly inhibit the germination of P. palmivora spores and damage to their structure. Moreover, chitosan activated the transcription of crucial genes and enzyme activities associated with the phenylpropane metabolism pathway in cassava roots, thus promoting rapid lignin accumulation and resulting in the early formation of a fracture layer. Chitosan was also found to enhance cassava root resistance by promoting the expression of pathogenesis-related proteins and the accumulation of flavonoids and total phenols. After 48 h of inoculation, cassava roots treated with chitosan exhibited a 51.4 % and 53.4 % decrease in lesion area for SC9 and SC6 varieties, respectively. The findings of this study offer a novel approach for managing postharvest deterioration of cassava roots.

Application of thermal imaging combined with machine learning for detecting the deterioration of the cassava root

Freshness is an important parameter that is indexed in the quality assessment of commercial cassava tubers. Cassava tubers that are not fresh have reduced starch content. Therefore, in this study, we aimed to develop a new approach to detect cassava root deterioration levels using thermal imaging with machine learning (ML). An underlying assumption was that nonfresh cassava roots may have fermentation inside that causes a difference in the inner temperature of the tuber. This creates the opportunity for the deterioration level to be measured using thermal imaging. The features (pixel intensity and temperature) that were extracted from the region of interest (ROI) in the form of tuber thermal images were analyzed with ML. Linear discriminant analysis (LDA), k-nearest neighbor (kNN), support vector machine (SVM), decision tree, and ensemble classifiers were applied to establish the optimal classification modeling algorithms. The highest accuracy model was developed from thermal images of cassava roots captured in a darkroom under a control temperature of 25 °C in the measurement chamber. The LDA, SVM, and ensemble classifiers gave the best overall performance for the discrimination of cassava root deterioration levels, with an accuracy of 86.7%. Interestingly, under uncontrolled environmental conditions, the combination of thermal imaging plus ML gave results that were of lower accuracy but still acceptable. Thus, our work revealed that thermal imaging coupled with ML was a promising method for the nondestructive evaluation of cassava root deterioration levels.

Comparison of Semen Aesculi species from different geographical origins and horse chestnut based on metabolomics and chemometrics analysis

BACKGROUND

Semen Aesculi, a traditional Chinese herbal medicine, has a long history of use for treating chest and abdominal pain with distension. In addition, the horse chestnut (Aesculus hippocastanum L.) is another species of Aesculus in Europe and has notable clinical significance in alleviating chronic venous insufficiency, hemorrhoids, and postoperative edema. Thus, highlighting the comparative study of Semen Aesculi and horse chestnut may broaden clinical applications.

OBJECTIVES

To conduct a comprehensive comparative analysis on the chemical profiling of these two varieties and determine whether they have equivalent clinical efficacy by integrating plant metabolomics and multivariate statistical methods.

METHODS

Initially, a comprehensive characterisation was performed using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS) platform, and in total 44 active ingredients were identified. Then, untargeted metabolomics combined with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) was applied for the discrimination of a German species and three official Chinese species. Next, 24 marker compounds responsible for the discrimination of different species were screened out and used to predict the species of unknown samples by genetic algorithm-optimised support vector machine (GA-SVM) with a high prediction accuracy. Finally, a heatmap visualisation was employed for clarifying the distribution of the identified active ingredients.

RESULTS

The three species of Chinese Semen Aesculi showed distinct separation from each other, while European horse chestnut and Aesculus chinensis Bunge were similar in chemical composition.

CONCLUSIONS

This work provided experimental evidence for further expanding the clinical application of Chinese Semen Aesculi and promoted the species identification and quality control of Semen Aesculi.

Cadmium influence on lipid metabolism in Sprague-Dawley rats through linoleic acid and glycerophospholipid metabolism pathways

Cadmium (Cd) is widely distributed in the environment and easy adsorbed by living organisms with adverse effects. Exposure to Cd-contaminated food may disrupt lipid metabolism and increase human health risk. To study the perturbation effect of Cd on lipid metabolism in vivo, 24 male Sprague-Dawley (SD) rats were randomly assigned four groups and treated by Cd chloride solution (0, 1.375 mg/kg, 5.5 mg/kg, 22 mg/kg) for 14 days. The characteristic indexes of serum lipid metabolism were analyzed. Afterwards, untargeted metabolomics analysis was applied to explore the adverse effects of Cd on rats by liquid chromatography coupled with mass spectrometry (LC-MS). The results revealed that Cd exposure obviously decreased the average serum of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) and caused an imbalance of endogenous compounds in the 22 mg/kg Cd-exposed group. Compared with the control group, 30 metabolites with significant differences were identified in the serum. Our results indicated that Cd caused lipid metabolic disorders in rats by disrupting linoleic acid and glycerophospholipid metabolism pathways. Furthermore, there were three kinds of remarkable differential metabolites-9Z,12Z-octadecadienoic acid, PC(20:4(8Z,11Z,14Z,17Z)/0:0), and PC(15:0/18:2(9Z,12Z)), which enriched the two significant metabolism pathways and could be the potential biomarkers.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Metabolic profiles of Sri Lankan cassava mosaic virus-infected and healthy cassava (Manihot esculenta Crantz) cultivars with tolerance and susceptibility phenotypes

BACKGROUND

Cassava mosaic disease (CMD) of cassava (Manihot esculenta Crantz) has expanded across many continents. Sri Lankan cassava mosaic virus (SLCMV; family Geminiviridae), which is the predominant cause of CMD in Thailand, has caused agricultural and economic damage in many Southeast Asia countries such as Vietnam, Loas, and Cambodia. The recent SLCMV epidemic in Thailand was commonly found in cassava plantations. Current understanding of plant-virus interactions for SLCMV and cassava is limited. Accordingly, this study explored the metabolic profiles of SLCMV-infected and healthy groups of tolerant (TME3 and KU50) and susceptible (R11) cultivars of cassava. Findings from the study may help to improve cassava breeding, particularly when combined with future transcriptomic and proteomic research.

RESULTS

SLCMV-infected and healthy leaves were subjected to metabolite extraction followed by ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS/MS). The resulting data were analyzed using Compound Discoverer software, the mzCloud, mzVault, and ChemSpider databases, and published literature. Of the 85 differential compounds (SLCMV-infected vs healthy groups), 54 were differential compounds in all three cultivars. These compounds were analyzed using principal component analysis (PCA), hierarchical clustering dendrogram analysis, heatmap analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Chlorogenic acid, DL-carnitine, neochlorogenic acid, (E)-aconitic acid, and ascorbyl glucoside were differentially expressed only in TME3 and KU50, with chlorogenic acid, (E)-aconitic acid, and neochlorogenic acid being downregulated in both SLCMV-infected TME3 and KU50, DL-carnitine being upregulated in both SLCMV-infected TME3 and KU50, and ascorbyl glucoside being downregulated in SLCMV-infected TME3 but upregulated in SLCMV-infected KU50. Furthermore, 7-hydroxycoumarine was differentially expressed only in TME3 and R11, while quercitrin, guanine, N-acetylornithine, uridine, vorinostat, sucrose, and lotaustralin were differentially expressed only in KU50 and R11.

CONCLUSIONS

Metabolic profiling of three cassava landrace cultivars (TME3, KU50, and R11) was performed after SLCMV infection and the profiles were compared with those of healthy samples. Certain differential compounds (SLCMV-infected vs healthy groups) in different cultivars of cassava may be involved in plant-virus interactions and could underlie the tolerance and susceptible responses in this important crop.

Analysis of Bitter Almonds and Processed Products Based on HPLC-Fingerprints and Chemometry

In the processing field, there is a saying that "seed drugs be stir-fried". Bitter almond (BA) is a kind of seed Chinese medicine. BA need be used after being fried. To distinguish raw bitter almonds (RBA) from processed products and prove the rationality of "seed drugs be stir-fried", we analyzed the RBA and five processed products (scalded bitter almonds, fried bitter almonds, honey fried bitter almonds, bran fried bitter almonds, bitter almonds cream) using RP-HPLC fingerprints and chemometric methods. The similarity between RBA and processed products was 0.733∼0.995. Hierarchically clustered heatmap was used to evaluate the changes in components. Principal component analysis (PCA) was used for classification, and all samples are distinguished according to RBA and five processing methods. Six chemical markers were obtained by partial least squares discriminant analysis (PLS-DA). The content and degradation rate of amygdalin and β-glucosidase activity were determined. Compared with RBA, the content and degradation rate of amygdalin, and β-glucosidase activity were increased in bitter almonds cream. The content and degradation rate were decreased, and β-glucosidase was inactivated in other processed products. The above results showed that stir-frying had the best effect. The results showed that processing can ensure the stability of RBA quality, and the saying "seed drugs be stir-fried" is reasonable.

Comparative chemical characters of Pseudostellaria heterophylla from geographical origins of China

Objective

Pseudostellaria heterophylla has been paid more attention in recent years, mainly as a medicine food homology plant. The content determination of P. heterophylla is not specified in the Chinese Pharmacopoeia (version 2020). The environmental conditions in different production areas could exert an influence on the quality of P. heterophylla. The purpose of this study is to discriminate P. heterophylla collected from different geographical origins of China.

Methods

In this study, the content of polysaccharide in 28 batches of P. heterophylla was determined using phenol-sulfuric acid. HPLC fingerprints were established under optimised HPLC-PDA methods. Subsequently, the similarity analysis (SA) and the quantification of heterophyllin B were analyzed. The metabolites of P. heterophylla were identified and evaluated using UHPLC-Q Exactive HF orbitrap MS system. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA) and orthogonal PLS-DA (OPLS-DA) were performed based on all peak areas.

Results

The polysaccharide content in Guizhou and Jiangsu was higher than that of other production areas, which varied significant from different origins. While the content of heterophyllin B in Anhui and Jiangsu was high. The correlation coefficients of HPLC fingerprints for 28 batches samples ranged from 0.877 to 0.990, and the characteristic map can be used to identify and evaluate the quality of P. heterophylla. The samples from Fujian, Guizhou, Jiangsu provinces can be relatively separated using multivariate statistical analysis including PCA, PLS-DA, HCA, OPLS-DA, indicating that their metabolic compositions were significantly different. Ultimately, a total of 15 metabolites which were filtrated by a VIP-value > 1 and a P-value < 0.05 associated with the separation of different origins were identified.

Conclusion

HPLC fingerprint was established to evaluate the quality and authenticity of P. heterophylla. The present work showed that the difference of geographic distributions had an influence on the internal chemical compositions. A sensitive and rapid untargeted metabolomics approach by UHPLC-Q Exactive HF orbitrap MS was utilized to evaluate P. heterophylla from different origins in China for the first time. Overall, this study provides insights to metabolomics of P. heterophylla and supplies important reference values for the development of functional foods.

Reactive oxygen species turnover, phenolics metabolism, and some key gene expressions modulate postharvest physiological deterioration in cassava tubers

Rapid postharvest physiological deterioration (PPD) in cassava (Manihot esculenta Crantz) tuber is a significant concern during storage. The freshly harvested tubers start spoiling within 24 to 72 h. Accumulation of H2O2 is one of the earliest biochemical events that occurred during PPD, which was detected using the 3,3 diaminobenzidine (DAB) in two contrast cassava genotypes, MNP Local A (29-57 μg g-1) and Sree Prakash (64-141 μg g-1). Accumulating the fluorescence hydroxycoumarin compounds emitted by the cassava tubers observed under an ultraviolet (UV) lamp showed significant variations at 0, 3, 6, 9, 12, and 15 days of storage. The total phenolics and carotenoids significantly and negatively correlated with PPD progression; however, the anthocyanin and flavonoids positively correlated with the PPD-anchored ROS accumulation. The primary compound, Phthalic acid, di(2-propylpentyl) ester, was identified in both the cassava tubers, Sree Prakash (57.21 and 35.21%), and MNP Local A (75.58 and 60.21%) at 0, and 72 h of PPD, respectively. The expression of PPD-associated genes APX-2, APX-3, PAL, and AP was higher at 6-12 days of PPD, which signified the synthesis of ROS turnover and phenylpropanoid biosynthesis. A significant, strong, and positive correlation was established between the secondary metabolites and PPD signaling gene expression, which was inversely correlated with hydroxycoumarin and H2O2 accumulation. MNP Local A tubers exhibited longer storage life of 15 days with a low PPD score, higher metabolites synthesis, and gene expression. The PPD-resistant lines may be used to augment cassava breeding strategies for large-scale commercial and industrial use.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

Comprehensive Quality Evaluation of the Root Bark of Morus alba L. Based on High-Performance Liquid Chromatography Fingerprinting and Chemometric Analyses

The quality of the root bark of Morus alba L. (SBP) herbs currently circulating in the market is variable. In order to ensure clinical effectiveness, a high-performance liquid chromatography (HPLC) fingerprinting method combined with chemical pattern recognition should be established to control the quality of SBP herbs. The differences of 23 batches of SBP were analyzed by exploratory cluster analysis based on shared fingerprint peak data, and the results indicated that the processing method to remove the cork layer from SBP materials is an important influencing factor on SBP quality. Principal component analysis indicated that SBP samples with the cork layer removed can be clearly distinguished from samples without cork layer removal. The potential chemical markers (kuwanon G, morusin and oxyresveratrol) were screened by partial least squares discriminant analysis. Finally, the contents of the main components were determined, indicating that the processing method of SBP materials can affect content of bioactive ingredients and that cork layer removal leads to a more uniform chemometric profile. The HPLC-based chemometrics approach described here will support the development of quality standards in SBP products.

Plant metabolomics: a new strategy and tool for quality evaluation of Chinese medicinal materials

The present quality control method of Chinese medicinal materials (CMM) has obvious deficiency, which cannot be compatible with the multi-target and multi-component characteristics and production process of CMM. Plant metabolomics with a huge impetus to comprehensively characterize the metabolites and clarify the complexity and integrity of CMM, has been widely used in the research of CMM. This article comprehensively reviewed the application of plant metabolomics in the quality control of CMM. It introduced the concept, technique, and application examples, discussed the prospects, limitations, improvements of plant metabolomics. MS and NMR, as important techniques for plant metabolomics, are mainly highlighted in the case references. The purpose of this article is to clarify the advantage of plants metabolomics for promoting the optimization of the CMM quality control system and proposing a system approach to realize the overall quality control of CMM based on plant metabolomics combined with multidisciplinary method.

Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification

Cassava is a crucial food and nutrition security crop cultivated by small-scale farmers because it can survive in a brutal environment. It is a significant source of carbohydrates in African countries. Sometimes, Cassava crops can be infected by leaf diseases, affecting the overall production and reducing farmers’ income. The existing Cassava disease research encounters several challenges, such as poor detection rate, higher processing time, and poor accuracy. This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN). The existing Standard CNN model utilizes extensive data processing features, increasing the computational overhead. A depth-wise separable convolution layer is utilized to resolve CNN issues in the proposed ECNN model. This feature minimizes the feature count and computational overhead. The proposed ECNN model utilizes a distinct block processing feature to process the imbalanced images. To resolve the color segregation issue, the proposed ECNN model uses a Gamma correction feature. To decrease the variable selection process and increase the computational efficiency, the proposed ECNN model uses global average election polling with batch normalization. An experimental analysis is performed over an online Cassava image dataset containing 6256 images of Cassava leaves with five disease classes. The dataset classes are as follows: class 0: “Cassava Bacterial Blight (CBB)”; class 1: “Cassava Brown Streak Disease (CBSD)”; class 2: “Cassava Green Mottle (CGM)”; class 3: “Cassava Mosaic Disease (CMD)”; and class 4: “Healthy”. Various performance measuring parameters, i.e., precision, recall, measure, and accuracy, are calculated for existing Standard CNN and the proposed ECNN model. The proposed ECNN classifier significantly outperforms and achieves 99.3% accuracy for the balanced dataset. The test findings prove that applying a balanced database of images improves classification performance.

A population based expression atlas provides insights into disease resistance and other physiological traits in cassava (Manihot esculenta Crantz)

Cassava, a food security crop in Africa, is grown throughout the tropics and subtropics. Although cassava can provide high productivity in suboptimal conditions, the yield in Africa is substantially lower than in other geographies. The yield gap is attributable to many challenges faced by cassava in Africa, including susceptibility to diseases and poor soil conditions. In this study, we carried out 3’RNA sequencing on 150 accessions from the National Crops Resources Research Institute, Uganda for 5 tissue types, providing population-based transcriptomics resources to the research community in a web-based queryable cassava expression atlas. Differential expression and weighted gene co-expression network analysis were performed to detect 8820 significantly differentially expressed genes (DEGs), revealing similarity in expression patterns between tissue types and the clustering of detected DEGs into 18 gene modules. As a confirmation of data quality, differential expression and pathway analysis targeting cassava mosaic disease (CMD) identified 27 genes observed in the plant–pathogen interaction pathway, several previously identified CMD resistance genes, and two peroxidase family proteins different from the CMD2 gene. Present research work represents a novel resource towards understanding complex traits at expression and molecular levels for the development of resistant and high-yielding cassava varieties, as exemplified with CMD.

Metabolomics of Chlorophylls and Carotenoids: Analytical Methods and Metabolome-Based Studies

Chlorophylls and carotenoids are two families of antioxidants present in daily ingested foods, whose recognition as added-value ingredients runs in parallel with the increasing number of demonstrated functional properties. Both groups include a complex and vast number of compounds, and extraction and analysis methods evolved recently to a modern protocol. New methodologies are more potent, precise, and accurate, but their application requires a better understanding of the technical and biological context. Therefore, the present review compiles the basic knowledge and recent advances of the metabolomics of chlorophylls and carotenoids, including the interrelation with the primary metabolism. The study includes material preparation and extraction protocols, the instrumental techniques for the acquisition of spectroscopic and spectrometric properties, the workflows and software tools for data pre-processing and analysis, and the application of mass spectrometry to pigment metabolomics. In addition, the review encompasses a critical description of studies where metabolomics analyses of chlorophylls and carotenoids were developed as an approach to analyzing the effects of biotic and abiotic stressors on living organisms.

Metabolomic and transcriptomic profiling reveals distinct nutritional properties of cassavas with different flesh colors

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A total of 508 metabolites were identified in three cassava cultivars.

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White-fleshed cassava had the highest contents of amino acids and organic acids.

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Yellow-fleshed cassava was enriched in metabolites related to specific pathways.

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Several pathways were found to be regulated at the transcriptional level.

Cassava is a significant food security crop in several developing countries. Metabolites in cassava roots provide numerous nutrients essential for human health. Exploiting the diversity of nutritional ingredients present in cassavas is vital for improving its nutritional value. To address this problem, root metabolomes of three cassava cultivars with white-flesh, light-yellow-flesh and yellow-flesh were comprehensively measured, respectively. A total of 508 metabolites were detected in cassava roots, including 300 primary metabolites and 185 secondary metabolites. There were 22.6% to 34.1% metabolites exhibiting significant variations among the three cassava cultivars. The light-yellow-flesh cassava contained higher contents of secondary metabolites, especially flavone, phenylpropanoids and alkaloids, and lower contents of primary metabolites except lipids, alcohols, vitamins and derivatives. Compared with light-yellow-flesh cassava, the yellow-flesh cassava contained higher contents of amino acid and derivatives, but lower contents of phenylpropanoids, nucleotide and derivates. White-flesh cassava contained higher contents of primary metabolites, especially amino acid and derivatives, but lower contents of secondary metabolites except flavonoid and indole derivatives. Transcriptome analyses were parallelly performed to decipher the potential mechanisms regulating the accumulations of related metabolites. Several pathways were both enriched by differentially expressed genes and differentially accumulated metabolites, supporting that metabolisms of these metabolites were regulated at transcriptional level. These results expand the knowledge on metabolite compositions in cassava roots and provide substantial information for genetic improvement of cassavas with high nutritional values.

Comparative Investigation for Raw and Processed Products of Euodiae Fructus Based on High-Performance Liquid Chromatography Fingerprints and Chemical Pattern Recognition

As a traditional Chinese medicine, Euodiae Fructus is widely used due to its analgesic, anti-inflammatory, and antihypertensive effects. However, Euodiae Fructus has also been documented to be toxic, and the toxic effects can be reduced by processing. To distinguish Euodiae Fructus from its processes products and study the changes of raw and processed products before and after processing, we evaluated four auxiliary material processing methods including vinegar, Zingiberis Rhizoma, Coptidis Rhizoma, and Glycyrrhizae Radix et Rhizoma. The raw Euodiae Fructus and four processed Euodiae Fructus samples were analyzed and compared based on the high-performance liquid chromatography (HPLC) fingerprints combined with chemometrics, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and principal component analysis-class (PCA-Class). A total of 27 common peaks were obtained by fingerprint analysis. The fingerprint similarity of raw and processed samples was between 0.86-0.999. We also determined the contents of the main active ingredients - Evodiamine and Rutaecarpine. PCA and PLS-DA analyses were used to distinguish between the raw and processed samples of Euodiae Fructus, and 14 chemical markers were screened out. Four kinds of processed products were further analyzed and the results showed that they could be successfully distinguished under the established models, and 12 chemical markers were labeled. PCA-Class results revealed that the classification models constructed in this study had adequate discrimination ability. The method combined with HPLC fingerprinting and multi-component chemical pattern recognition technology could be used to differentiate raw and processed Euodiae Fructus with adequate predictive power. Our findings confirmed the rationality of the pharmacopoeial method and provided a reference for the quality control of the Glycyrrhizae Radix et Rhizoma processed Euodiae Fructus.

Character changes and Transcriptomic analysis of a cassava sexual Tetraploid

BACKGROUND

Cassava (Manihot esculenta Crantz) is an important food crop known for its high starch content. Polyploid breeding is effective in its genetic improvement, and use of 2n gametes in sexual polyploid breeding is one of the potential methods for cassava breeding and improvement. In our study, the cassava sexual tetraploid (ST), which carries numerous valuable traits, was successfully generated by hybridizing 2n female gametes SC5 (♀) and 2n male gametes SC10 (♂). However, the molecular mechanisms remain unclear. To understand these underlying molecular mechanisms behind the phenotypic alterations and heterosis in ST plants, we investigated the differences in gene expression between polyploids and diploids by determining the transcriptomes of the ST plant and its parents during the tuber root enlargement period. We also compared the characters and transcriptomes of the ST plant with its parents.

RESULTS

The ST plant was superior in plant height, stem diameter, leaf area, petiole length, plant weight, and root weight than the parent plants, except the leaf number, which was lower. The number of starch granules was higher in the roots of ST plants than those in the parent plants after five months (tuber root enlargement period), which could be due to a higher leaf net photosynthetic rate leading to early filling of starch granules. Based on transcriptome analysis, we identified 2934 and 3171 differentially expressed genes (DEGs) in the ST plant as compared to its female and male parents, respectively. Pathway enrichment analyses revealed that flavonoid biosynthesis and glycolysis/gluconeogenesis were significantly enriched in the ST plants, which might contribute to the colors of petiole (purple-red), root epidermis (dark brown), and tuber starch accumulation, respectively.

CONCLUSIONS

After sexual polyploidization, the phenotype of ST has changed significantly in comparison to their diploid parents, mainly manifest as enlarged biomass, yield, early starch filling, deep colored petiole and root epidermis. The tetraploid plants were also mature early due to early starch grain filling. Owing to enriched flavonoid biosynthesis and glycolysis/gluconeogenesis, they are possibly resistant to adversity stresses and provide better yield, respectively.

Enhancing the Shelf-Life of Fresh Cassava Roots: A Field Evaluation of Simple Storage Bags

Postharvest physiological deterioration (PPD) of fresh cassava roots limits their shelf-life to about 48 h. There is a demand for simple, cheap, and logistically feasible solutions for extending the shelf life of fresh cassava roots in industrial processes. In this study, three different types of bag materials were tested, namely woven polypropylene, tarpaulin, and jute as a potential storage solution for cassava roots with different levels of mechanical damage. Microclimate related to temperature, humidity, and carbon di-oxide (CO2) was monitored in order to understand the storage conditions for up to 12 days. The results showed that fresh cassava roots could be stored for 8 days, with minimal PPD and starch loss (2.4%). However, roots with significant mechanical damage in the form of cuts and breakages had a considerably shorter shelf life in the storage bag, compared to whole roots and roots with retained stalk (peduncle) where roots are connected to the main plant. Wetting of the roots and bag material were not significant factors in determining the shelf life and starch loss. Carbon dioxide concentration in the stores was significantly correlated with the starch loss in fresh cassava roots and is proposed as a possible method for continuously and remotely monitoring starch loss in large-scale commercial operations and reducing postharvest losses.

Evaluating the effects of temperature and time on heterocyclic aromatic amine profiles in roasted pork using combined UHPLC-MS/MS and multivariate analysis

In this study, ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) combined with principal component analysis (PCA) were used to investigate the effects of process conditions on the profiles of carcinogenic and mutagenic heterocyclic aromatic amine (HAA) in the pork roasted at 175 °C, 200 °C, 225 °C and 250 °C for 10, 15, 20, 25, 30, 35 and 40 min. Twelve HAAs from four categories, including carboline (Norharman, Harman, and Phe-p-1), imidazopyridine (PhIP, 4'-OH-PhIP, DMIP, and 1,5,6-TMIP), imidazoquinoline (IQ, IQ [4,5-b], and MeIQ), and imidazoquinoxaline (MeIQx and 4,8-DiMeIQx), were detected, quantified and used to compose the HAA profiles in roasted pork. After being Analyzed by PCA, the distributions of HAA profiles from different temperature on the PCA score plot demonstrated that there are significant differences among the HAA profiles from different temperatures. The loading plot also showed that PhIP, 4'-OH-PhIP, IQ[4,5-b], and MeIQ were mainly responsible for the difference. The profiles from higher temperature distribute more scattered than the lower ones, illustrating that the time effects on the HAA profiles from higher temperature are stronger than the lower ones. Comparing the score and loading plots of different heating times, the diversities of the HAA profiles at different temperatures increased under prolonged heating because of the changingpyridines levels. The results of PCA that comparing the HAA from different categories displayed that the formation features of four categories HAAs were significantly differed because of their formation discrepancy under low temperatures and short-term roasting. Using HAA profiles as an entirety, these findings obtained in this study are more close to the real process of HAA formation in roasted pork, and make the complex effects of temperature and time on multiple HAA formations more simply to be concluded.

Highly dynamic, coordinated, and stage-specific profiles are revealed by a multi-omics integrative analysis during tuberous root development in cassava

Cassava (Manihot esculenta) is an important starchy root crop that provides food for millions of people worldwide, but little is known about the regulation of the development of its tuberous root at the multi-omics level. In this study, the transcriptome, proteome, and metabolome were examined in parallel at seven time-points during the development of the tuberous root from the early to late stages of its growth. Overall, highly dynamic and stage-specific changes in the expression of genes/proteins were observed during development. Cell wall and auxin genes, which were regulated exclusively at the transcriptomic level, mainly functioned during the early stages. Starch biosynthesis, which was controlled at both the transcriptomic and proteomic levels, was mainly activated in the early stages and was greatly restricted during the late stages. Two main branches of lignin biosynthesis, coniferyl alcohol and sinapyl alcohol, also functioned during the early stages of development at both the transcriptomic and proteomic levels. Metabolomic analysis further supported the stage-specific roles of particular genes/proteins. Metabolites related to lignin and flavonoid biosynthesis showed high abundance during the early stages, those related to lipids exhibited high abundance at both the early and middle stages, while those related to amino acids were highly accumulated during the late stages. Our findings provide a comprehensive resource for broadening our understanding of tuberous root development and will facilitate future genetic improvement of cassava.

Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods

Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Effect of pulsed light on postharvest disease control-related metabolomic variation in melon (Cucumis melo) artificially inoculated with Fusarium pallidoroseum

Pulsed light, as a postharvest technology, is an alternative to traditional fungicides, and can be used on a wide variety of fruit and vegetables for sanitization or pathogen control. In addition to these applications, other effects also are detected in vegetal cells, including changes in metabolism and secondary metabolite production, which directly affect disease control response mechanisms. This study aimed to evaluate pulsed ultraviolet light in controlling postharvest rot, caused by Fusarium pallidoroseum in 'Spanish' melon, in natura, and its implications in disease control as a function of metabolomic variation to fungicidal or fungistatic effects. The dose of pulsed light (PL) that inhibited F. pallidoroseum growth in melons (Cucumis melo var. Spanish) was 9 KJ m-2. Ultra-performance liquid chromatography (UPLC) coupled to a quadrupole-time-of-flight (QTOF) mass analyzer identified 12 compounds based on tandem mass spectrometry (MS/MS) fragmentation patterns. Chemometric analysis by Principal Components Analysis (PCA) and Orthogonal Partial Least Squared Discriminant Analysis (OPLS-DA) and corresponding S-Plot were used to evaluate the changes in fruit metabolism. PL technology provided protection against postharvest disease in melons, directly inhibiting the growth of F. pallidoroseum through the upregulation of specific fruit biomarkers such as pipecolic acid (11), saponarin (7), and orientin (3), which acted as major markers for the defense system against pathogens. PL can thus be proposed as a postharvest technology to prevent chemical fungicides and may be applied to reduce the decay of melon quality during its export and storage.

Plant Metabolomics Using NMR Spectroscopy

The major goal in plant metabolomics is to study complex extracts for the purposes of metabolic exploration and natural products discovery. To achieve this goal, plant metabolomics relies on accurate and selective acquisition of all possible chemical information, which includes maximization of the number of detected metabolites and their correct molecular assignment. Nuclear magnetic resonance (NMR) spectroscopy has been recognized as a powerful platform for obtaining the metabolite profiles of plant extracts. In this chapter, we provide a workflow for targeted and untargeted metabolite profiling of plant extracts using both 1D and 2D NMR methods. The protocol includes sample preparation, instrument operation, data processing, multivariate analysis, biomarker elucidation, and metabolite quantitation. It also addresses the annotation of plant metabolite peaks considering NMR's capabilities to cover a broad range of metabolites and elucidate structures for unknown compounds.

Modelling the vigour of maize seeds submitted to artificial accelerated ageing based on ATR-FTIR data and chemometric tools (PCA, HCA and PLS-DA)

The main goals of this research were to use ATR-FTIR spectroscopy associated with multivariate analyses to identify biochemical changes in high and low vigour seed tissues (embryo and endosperm) in response to accelerated ageing and to create a model to predict seed vigour based on spectroscopic data. High-vigour seeds undergo minimal changes in biochemical composition during stress by accelerated ageing while low-vigour seeds are more sensitive to stress and this lower tolerance is associated with reduced lipid and protein content and increased amino acids, carbohydrates and phosphorus compounds in the embryo. High-vigour seeds show an increase in peaks associated with amino acids and phosphorous compounds in the endosperm after 24 h of stress while low-vigour seeds present these high-intensity peaks only after 72 h in the embryo. The results of this research provide the theoretical basis for the genetic improvement of maize cultivars that aim at higher physiological seed quality.

Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging

Seed vitality is one of the primary determinants of high yield that directly affects the performance of seedling emergence and plant growth. However, seed vitality may be lost during storage because of unfavorable conditions, such as high moisture content and temperatures. It is therefore vital for seed companies as well as farmers to test and determine seed vitality to avoid losses of any kind before sowing. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with multiple data preprocessing methods and classification models was applied to identify the vitality of rice seeds. A total of 2400 seeds of three different years: 2015, 2016 and 2017, were evaluated. The experimental results show that the NIR-HSI technique has great potential for identifying vitality and vigor of rice seeds. When detecting the seed vitality of the three different years, the extreme learning machine model with Savitzky-Golay preprocessing could achieve a high classification accuracy of 93.67% by spectral data from only eight wavebands (992, 1012, 1119, 1167, 1305, 1402, 1629 and 1649 nm), which could be developed for a fast and cost-effective seed-sorting system for industrial online application. When identifying non-viable seeds from viable seeds of different years, the least squares support vector machine model coupled with raw data and selected wavelengths of 968, 988, 1204, 1301, 1409, 1463, 1629, 1646 and 1659 nm achieved better classification performance (94.38% accuracy), and could be adopted as an optimal combination to identify non-viable seeds from viable seeds.

Profiling of volatile and non-volatile metabolites in Polianthes tuberosa L. flowers reveals intraspecific variation among cultivars

Polianthes tuberosa L. (tuberose) is a widely cultivated ornamental crop in Asian countries. Different cultivars of tuberose have been developed through breeding programs in India. However, no reports on floral fragrance and metabolite contents of these cultivars are available. In this study, an attempt has been made to evaluate the levels of both volatile and non-volatile metabolites from seven different cultivars of P. tuberosa. Presence of benzenoids, phenylpropanoids, terpenoids, and few fatty acid derivatives as emitted, endogenous and glycosylated forms were revealed from the studied cultivars. Further, chemometric analyses in both supervised and unsupervised manner led to identification of patterns among the cultivars. Among the seven cultivars, four distinct clusters were obtained linking to their volatiles, flavonoids and primary metabolite levels. Metabolic variations obtained from the cultivars also suggest cross-talks between phenylpropanoid, benzenoid, and flavonoid pathways. Thus metabolite profiling reported here may help in characterization of tuberose cultivars for perfumery utility and future breeding programme.

Association of preharvest management with oxidative protection and enzymatic browning in minimally processed cassava

The aim of this study was to examine oxidative protection and enzymatic browning in the storage of minimally processed cassava and their relationship with population density and harvest age. Population densities were 1.0, 1.25, 1.5, and 1.75 plants m^-2 . After being harvested at 300, 360, or 420 days after planting, cassava were minimally processed and stored at 5 ± 2°C. It was observed that superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) play key roles in the tolerance of young roots to browning. Planting density, however, does not appear to be a key factor modulating the activity of the enzymes studied. PRACTICAL APPLICATIONS: Younger harvested cassava roots, harvested at 300 days, are more tolerant to enzymatic browning. This appears to be in part due to enzymatic activity modulation of the SOD, CAT, and POD enzymes. In addition, it has been demonstrated that agronomic techniques aimed at increasing productivity, such as increasing the planting density of cassava, do not alter the biomarkers of postharvest quality. In summary, evidence that field management may be an efficient approach to improving the conservation of minimally processed cassava is provided. We believe that the findings of this paper will be of great interest regarding the influence of field management on the postharvest quality of freshly cut cassava and will also provide applicable results relating to its production chain.

Comprehensive quality assessment of Dendrubium officinale using ATR-FTIR spectroscopy combined with random forest and support vector machine regression

Dendrobium officinale, as a tonic herb, has attracted more and more consumers to consume in daily life. In order to protect the wild resource, the herb has made great progress though cultivation in vitro. However, the quality is fluctuated in Chinese herbal medicine market due to influence such as cultivated areas and harvesting period. Therefore, the herbal samples from different cultivated locations were evaluated with high-performance liquid chromatography with diode array detector (HPLC-DAD) in terms of two chemical components, quercetin and erianin. In addition, two markers in leaf and stem also were used for support vector machine regression (SVMR) prediction. Samples from different harvesting periods were also classified using attenuated total reflectance mid-infrared spectroscopy coupled with random forest model. The results indicated that Pu'er and Menghai in Yunnan Province were suitable places for the herb cultivation and the leaf of the herb was also an exploitable resource just in light of the content of two components. What's more, combination of suitable spectra pretreatment and grid search method efficiently improved the prediction performance of the regression model. The results of random forest model indicated that important variables combination between stem and leaf was an effective tool to predict the harvesting time of the herb with 94.44% accuracy in calibration set and 97.92% classification correct rate in validation set. The results of combination were better than the models using individual stem and leaf spectra. In addition, the suitable harvesting time (December) could be classified efficiently. Our study provides a reference for quality control of raw materials from D. officinale herb.

Evaluation of the Starch Quantification Methods of Musa paradisiaca, Manihot esculenta, and Dioscorea trífida Using Factorial Experiments

Background

Starch and its products are used in a variety of ways for both the food and nonfood industries. A factorial experiment is carried out with two factors to explain the behavior of the percentage of starch, where the factors correspond to the extraction method and to the raw material.

Method

Three methods were used in triplicate: the first followed the official technique of the Association of Official Analytical Chemists (AOAC), to perform acid hydrolysis and quantification of starch by Titulation; the second method involved the colorful reaction with iodine using the UV equipment to measure the absorbance and calculate the percentage of starch; as a third method the FTIR was used, through which the concentration of the starch was calculated by the area under the curve obtained from the spectrum.

Results

there is an effect of both the method and the raw material on the percentage of starch, while there was no effect of the interaction; the Tukey test indicates that the highest average percentage of extraction occurs with the extraction method by Titulation and with the starch of Manihot esculenta.

Conclusion

It is used as raw material. The method of quantification of starch by UV-VIS spectroscopy was the best for the study samples because it presented less deviation in relation to the FTIR and Titulation methods.

Classification of samples from NMR-based metabolomics using principal components analysis and partial least squares with uncertainty estimation

Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure ( https://doi.org/10.1016/j.chemolab.2016.12.010 ) with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing. Graphical abstract ᅟ.