Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice

Simultaneous determination of subspecies and geographic origins of 110 rice cultivars by microsatellite markers

Rice varieties of different subspecies types (indica rice and japonica rice) across various geographical origins (Hunan, Jiangsu, and Northeast China) were monitored using microsatellite markers (simple sequence repeats, SSR). 110 representative rice cultivars were collected from the main crop areas. Multiple methods including clustering analysis (neighbor-joining (NJ) method, unweighted pair-group method with arithmetic mean (UPGMA) method), principal component analysis (PCA) and model-based grouping were applied. The study revealed that 25 pairs of SSR markers exhibited a broad range of polymorphism information content (PIC) values, ranging from 0.240 to 0.830. Furthermore, our study successfully achieved a higher overall mean correct rate of 99.09% in determining the geographical origin of rice. Simultaneously, it accurately classified indica rice and japonica rice. These findings are significant as they provide an SSR fingerprint of 110 high-quality rice cultivars, serving as a valuable scientific resource for the detection of rice adulteration and traceability of its origin.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

A Critical Overview of HPLC-MS-Based Lipidomics in Determining Triacylglycerol and Phospholipid in Foods

With the current advancement in mass spectrometry (MS)-based lipidomics, the knowledge of lipidomes and their diverse roles has greatly increased, enabling a deeper understanding of the action of bioactive lipid molecules in plant- and animal-based foods. This review provides in-depth information on the practical use of MS techniques in lipidomics, including lipid extraction, adduct formation, MS analysis, data processing, statistical analysis, and bioinformatics. Moreover, this contribution demonstrates the effectiveness of MS-based lipidomics for identifying and quantifying diverse lipid species, especially triacylglycerols and phospholipids, in foods. Further, it summarizes the wide applications of MS-based lipidomics in food science, such as for assessing food processing methods, detecting food adulteration, and measuring lipid oxidation in foods. Thus, MS-based lipidomics may be a useful method for identifying the action of individual lipid species in foods.

Nutritional lipidomics for the characterization of lipids in food

Lipids represent one out of three major macronutrient classes in the human diet. It is estimated to account for about 15-20% of the total dietary intake. Triacylglycerides comprise the majority of them, estimated 90-95%. Other lipid classes include free fatty acids, phospholipids, cholesterol, and plant sterols as minor components. Various methods are used for the characterization of nutritional lipids, however, lipidomics approaches become increasingly attractive for this purpose due to their wide coverage, comprehensiveness and holistic view on composition. In this chapter, analytical methodologies and workflows utilized for lipidomics profiling of food samples are outlined with focus on mass spectrometry-based assays. The chapter describes common lipid extraction protocols, the distinct instrumental mass-spectrometry based analytical platforms for data acquisition, chromatographic and ion-mobility spectrometry methods for lipid separation, briefly mentions alternative methods such as gas chromatography for fatty acid profiling and mass spectrometry imaging. Critical issues of important steps of lipidomics workflows such as structural annotation and identification, quantification and quality assurance are discussed as well. Applications reported over the period of the last 5years are summarized covering the discovery of new lipids in foodstuff, differential profiling approaches for comparing samples from different origin, species, varieties, cultivars and breeds, and for food processing quality control. Lipidomics as a powerful tool for personalized nutrition and nutritional intervention studies is briefly discussed as well. It is expected that this field is significantly growing in the near future and this chapter gives a short insight into the power of nutritional lipidomics approaches.

CRISPR/Cas9 Based Site-Specific Modification of FAD2 cis-Regulatory Motifs in Peanut (Arachis hypogaea L)

Peanut (Arachis hypogaea L.) seed is a rich source of edible oil, comprised primarily of monounsaturated oleic acid and polyunsaturated linoleic acid, accounting for 80% of its fatty acid repertoire. The conversion of oleic acid to linoleic acid, catalyzed by Fatty Acid Desaturase 2 (FAD2) enzymes, is an important regulatory point linked to improved abiotic stress responses while the ratio of these components is a significant determinant of commercial oil quality. Specifically, oleic acid has better oxidative stability leading to longer shelf life and better taste qualities while also providing nutritional based health benefits. Naturally occurring FAD2 gene knockouts that lead to high oleic acid levels improve oil quality at the potential expense of plant health though. We undertook a CRISPR/Cas9 based site-specific genome modification approach designed to downregulate the expression of two homeologous FAD2 genes in seed while maintaining regulation in other plant tissues. Two cis-regulatory elements the RY repeat motif and 2S seed protein motif in the 5′UTR and associated intron of FAD2 genes are potentially important for regulating seed-specific gene expression. Using hairy root and stable germ line transformation, differential editing efficiencies were observed at both CREs when targeted by single gRNAs using two different gRNA scaffolds. The editing efficiencies also differed when two gRNAs were expressed simultaneously. Additionally, stably transformed seed exhibited an increase in oleic acid levels relative to wild type. Taken together, the results demonstrate the immense potential of CRISPR/Cas9 based approaches to achieve high frequency targeted edits in regulatory sequences for the generation of novel transcriptional alleles, which may lead to fine tuning of gene expression and functional genomic studies in peanut.

Lipidomic profiling reveals molecular modification of lipids in hepatopancreas of juvenile mud crab (Scylla paramamosain) fed with different dietary DHA/EPA ratios

Untargeted lipidomic analysis was conducted to explore how different dietary docosahexaenoic acid (DHA)/eicosapentaenoic acid (EPA) ratio and, specifically, how an optimal ratio (2.3) compared to a suboptimum ratio (0.6) impacted lipid molecular species and the positional distribution of fatty acids in hepatopancreas of mud crab. The results indicated that major category of lipid affected by dietary DHA/EPA ratio was glycerophospholipids (GPs). The optimum dietary DHA/EPA ratio increased the contents of DHA bound to the sn-2 and sn-3 positions of phosphatidylcholine (PC) and triacylglycerol, EPA bound to the sn-2 position of phosphatidylcholine and 18:2n-6 bound to the sn-2 position of phosphatidylethanolamine (PE). Increased dietary DHA/EPA ratio also led to competition between arachidonic acid (ARA) and 18:2n-6 bound to esterified sites. Appropriate dietary DHA/EPA ratio can not only improve the growth performance and nutritional quality of mud crab, but also provide higher quality products for human consumers.

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Comprehensive Lipidomics Analysis Reveals the Effects of Different Omega-3 Polyunsaturated Fatty Acid-Rich Diets on Egg Yolk Lipids

Dietary supplementation with omega-3 polyunsaturated fatty acids (n-3 PUFAs) for laying hens enriches eggs with these essential fatty acids. However, the enrichment patterns and changes to intact lipids in egg yolk have not been sufficiently revealed. Herein, egg yolk lipids from hens fed with diets supplemented with flaxseed, Schizochytrium sp. residue, or their mixture were comprehensively analyzed using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). A total of 335 individual lipid species covering 23 (sub)classes were identified and quantified. Distinct n-3 PUFA-lipid profiles were revealed among different groups. Dietary α-linolenic acid (ALA) was mainly deposited in the TAG fraction, whereas synthesized or preformed docosahexaenoic acid (DHA) predominantly existed in the glycerophospholipid form. Furthermore, different lipid species were identified and related lipid pathways after dietary supplementation were analyzed. Collectively, these findings provide us with new knowledge for production, nutritional evaluation, authentication, and application of n-3 PUFA-enriched eggs.

Fraud vulnerability quantitative assessment of Wuchang rice industrial chain in China based on AHP-EWM and ANN methods

Vulnerability assessment has been used in the food fraud mitigation based on the subjective judgement of industry participants and simple calculation. To have a more objective result, an improved vulnerability quantitative assessment method was proposed. The overall fraud vulnerability was described by the vulnerability of fraud factors and the health and economic impact of fraud incidents. The fraud factors were related to opportunity, motivation and control measure. Analytic hierarchy process combined with entropy weighting method (AHP-EWM) and artificial neural networking (ANN) to improve judgment accuracy. In the application in Wuchang rice industrial chain, 51 fraud factors were used in the assessment and 10 experts, 36 farmers, 15 suppliers and 15 supervisors were interviewed. Results showed that Wuchang rice industrial chain was highly vulnerable to fraud. The opportunity for fraud was high, the motivation to commit it was moderate, and controls to prevent it needed reinforcing. Fraud vulnerability differed between farmers and suppliers. To reduce the fraud vulnerability, improved regulations and policies and stiffer penalties were strongly recommended.

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.

Comparative review and the recent progress in detection technologies of meat product adulteration

Meat adulteration, mainly for the purpose of economic pursuit, is widespread and leads to serious public health risks, religious violations, and moral loss. Rapid, effective, accurate, and reliable detection technologies are keys to effectively supervising meat adulteration. Considering the importance and rapid advances in meat adulteration detection technologies, a comprehensive review to summarize the recent progress in this area and to suggest directions for future progress is beneficial. In this review, destructive meat adulteration technologies based on DNA, protein, and metabolite analyses and nondestructive technologies based on spectroscopy were comparatively analyzed. The advantages and disadvantages, application situations of these technologies were discussed. In the future, determining suitable indicators or markers is particularly important for destructive methods. To improve sensitivity and save time, new interdisciplinary technologies, such as biochips and biosensors, are promising for application in the future. For nondestructive techniques, convenient and effective chemometric models are crucial, and the development of portable devices based on these technologies for onsite monitoring is a future trend. Moreover, omics technologies, especially proteomics, are important methods in laboratory detection because they enable multispecies detection and unknown target screening by using mass spectrometry databases.

Comparative lipid profile of four edible shellfishes by UPLC-Triple TOF-MS/MS

An ultra performance liquid chromatography-Triple time of flight mass spectrometry (UPLC-Triple TOF-MS/MS) method were established to characterize the lipid profiles in four shellfish species. More than 600 lipid molecular species belonging to 14 classes were detected. Phospholipids (PLs) were predominant in Chlamys farreri (54.9%) and glycerolipids (GLs) were dominant in Ostrea gigas (51.6%). PLs that contained polyunsaturated fatty acids (PUFAs) such as PC (16:0/20:5), PC (16:0/22:6) and PE (18:0/22:6) were the main molecular species. Especially, the percentage of sphingolipids (SLs) in four shellfishes is considerable (18.8-38.6%), the characterization of their special long-chain base (LCB) structure (mainly d19:3) and N-acyl group (mainly 16:0) was realized. Several SL subclasses with low abundance in four shellfish species, such as ceramide 2-aminoethylphosphonate (CAEP) and deoxy-ceramide (DeoxyCer), were also detected. These active lipids identified by this method have potential value in revealing the nutritional value of shellfishes and serving as biomarkers for distinguishing different shellfishes.

Enzymatic Production and Characterization of Pectic Oligosaccharides Derived from Citrus and Apple Pectins: A GC-MS Study Using Random Forests and Association Rule Learning

Pectic oligosaccharides (POS) from citrus and apple pectin hydrolysis using ViscozymeL and Glucanex200G have been obtained. According to the results, maximum POS formation was achieved from citrus pectin after 30 min of hydrolysis with ViscozymeL, with a yield of 652 mg g^-1 and average molecular mass ( M_w) of 0.8-2.5 kDa, while with Glucanex200G, the yield was 518 mg g^-1 and M_w was 0.8-7.1 kDa. Digalacturonic and trigalacturonic acids were identified among other low M_w compounds as di- and tri-POS. In addition, differences in GC-MS spectra of all oligosaccharides found in the hydrolysates were studied by employing random forests and other algorithms to identify structural differences between the obtained POS, and high prediction rates were shown for new samples. Chemical structures were proposed for some influential m/ z ions, and 12 association rules that explain differences according to pectin and enzyme origin were built. This information could be used to establish structure-function relationships of POS.

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.

Automated classification of food products using 2D low-field NMR

In this work, low-field proton (¹H) and sodium (²³Na) relaxation and diffusion measurements are used to detect and classify different types of food products. A compact and low-cost system based on a small 0.5 T permanent magnet has been developed to autonomously authenticate such products. The system uses a simple but efficient double-tuned matching network suitable for ¹H/²³Na NMR. Various machine learning algorithms are used to classify food samples based on T₁-T₂ and D-T₂ data generated by the system, and the accuracy and prediction speed of these algorithms are studied in detail. The influence of temperature drift upon prediction accuracy is also studied. Experimental results demonstrate reliable classification of cooking oils, milk, and soy sauces.

The integration of multi-platform MS-based metabolomics and multivariate analysis for the geographical origin discrimination of Oryza sativa L

For the authentication of white rice from different geographical origins, the selection of outstanding discrimination markers is essential. In this study, 80 commercial white rice samples were collected from local markets of Korea and China and discriminated by mass spectrometry-based untargeted metabolomics approaches. Additionally, the potential markers that belong to sugars & sugar alcohols, fatty acids, and phospholipids were examined using several multivariate analyses to measure their discrimination efficiencies. Unsupervised analyses, including principal component analysis and k-means clustering demonstrated the potential of the geographical classification of white rice between Korea and China by fatty acids and phospholipids. In addition, the accuracy, goodness-of-fit (R²), goodness-of-prediction (Q²), and permutation test p-value derived from phospholipid-based partial least squares-discriminant analysis were 1.000, 0.902, 0.870, and 0.001, respectively. Random Forests further consolidated the discrimination ability of phospholipids. Furthermore, an independent validation set containing 20 white rice samples also confirmed that phospholipids were the excellent discrimination markers for white rice between two countries. In conclusion, the proposed approach successfully highlighted phospholipids as the better discrimination markers than sugars & sugar alcohols and fatty acids in differentiating white rice between Korea and China.