Search

Objective To explore the potential biomarkers for the diagnosis of primary brain stem injury （PBSI） by using metabonomics method to observe the changes of metabolites in rats with PBSI caused death. Methods PBSI, non-brain stem brain injury and decapitation rat models were established, and metabolic maps of brain stem were obtained by LC-MS metabonomics method and annotated to the HMDB database. Partial least square-discriminant analysis （PLS-DA） and random forest methods were used to screen potential biomarkers associated with PBSI diagnosis. Results Eighty-six potential metabolic markers associated with PBSI were screened by PLS-DA. They were modeled and predicted by random forest algorithm with an accuracy rate of 83.3%. The 818 metabolic markers annotated to HMDB database were used for random forest modeling and prediction, and the accuracy rate was 88.9%. According to the importance in the identification of cause of death, the most important metabolic markers that were significantly up-regulated in PBSI group were HMDB0038126 （genipinic acid, GA）, HMDB0013272 （N-lauroylglycine）, HMDB0005199 [（R）-salsolinol] and HMDB0013645 （N,N-dimethylsphingosine）. Conclusion GA, N-lauroylglycine, （R）-salsolinol and N,N-dimethylsphingosine are expected to be important metabolite indicators in the diagnosis of PBSI caused death, thus providing clues for forensic medicine practice.

Copyright of Journal of Forensic Medicine / Fayixue Zazhi is the property of Journal of Forensic Medicine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobility is crucial for healthy aging. Any disruption to mobility can affect mental, physical and social health, and socio-economic independence. Therefore, studies in gait and lower-joint functionality with respect to different demographic features will play a vital role in maintaining good mobility. In this study, we analyzed a gait database from 70 healthy subjects (18–86 years) constructed using our custom-built multi-sensor-based wearable tele-health monitoring system. The purpose was to extract and use the most informative features for classifying knee joint and gait characteristics of the subjects with respect to their age, body mass index – BMI, and sex. Four supervised machine learning algorithms: partial least square-discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were used to classify the subjects. The features that significantly contributed to all classifications are knee angle, quadriceps muscle pressure adjacent to the knee joint, rotational energy (mediolateral and vertical), acceleration energy (mediolateral), cross-sample entropy (anteroposterior-mediolateral), knee angle variability, symmetry of swing and stance phase, and walk ratio. Classification accuracies of all four methods were ~89%, 83%, 81%, 86% for age, 90%, 80%, 83%, 86% for BMI, and 97%, 97%, 96%, 97% for sex, respectively. PLS-DA had the best classification performance for all three categories which makes it preferable for these kinds of analyses. Thus, our knee and gait monitoring system coupled with an efficient machine learning tool can be exploited for real-time evaluation and early diagnoses of mobility disabilities, health assessment, and monitoring the need for interventions. [ABSTRACT FROM AUTHOR]

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat., Competing Interests: The authors declare no potential conflicts of interest., (© Korean Society for Food Science of Animal Resources.)

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness. [ABSTRACT FROM AUTHOR]

International Olive Oil Council (IOOC) states chemical and organolep- tic parameters to classify the commercial grade of olive oil. Finding tools or analytical procedures able to support the organoleptic evaluation would be helpful to streamline and facilitate the commercial classification. The aim of the present study was to evaluate a new tool and validate a procedure that allows a fast and non-invasive volatile compounds detection system, able to assign each sample to its right trade category. Moreover, we tried to test the capabili- ty of PTR-ToF-MS in grading olive oils according to their fruity intensity levels. A total of 273 olive oil samples collected from Argentina (21), Chile (10), Italy (191), Morocco (17), Tunisia (4) and EU (30) were analyzed and classified through: (1) Panel Test and (2) PTR-ToF-MS analysis. On the whole PTR-ToF-MS data EVOO and Not EVOO as resulted by Panel Test were clustered by PCA in two main groups and correctly classified by PLS-DA model, confirming the high confidence level (95%) in utilizing analytical spectral data for helping Panel Test and able to easy monitoring the quality formation in the oils, by a fast and cheap control from harvest until the store. The eight protonated masses detected as VIP by the model may be linked to negative olfactory notes. Finally, PCA applied on the volatile profile of 122 classified EVOO highlighted a shift of the samples distribution following the trend of the fruity intensity as assessed by the panelists. In conclusion, this trial confirmed the availability of a new, precise and simple analytical tool as the PTR-ToF-MS, which coupled with an appropriate multivariate data analysis, allows to classify EVOO according to their trade category and fruity intensity.

The aim here was to explore the potential of visible and near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) to classify fresh chicken breast fillets into different water-holding capacity (WHC) groups. Initially, the extracted spectra and image textural features, as well as the mixed data of the two, were used to develop partial least square-discriminant analysis (PLS-DA) classification models. Smoothing, a first derivative process, and principle component analysis (PCA) were carried out sequentially on the mean spectra of all samples to deal with baseline offsets and identify outlier data. Six samples located outside the confidence ellipses of 95% confidence level in the score plot were defined as outliers. A PLS-DA model based on the outlier-free spectra provided a correct classification rate (CCR) value of 78% in the prediction set. Then, seven optimal wavelengths selected using a successive projections algorithm (SPA) were used to develop a simplified PLS-DA model that obtained a slightly reduced CCR with a value of 73%. Moreover, the gray-level co-occurrence matrix (GLCM) was implemented on the first principle component image (with 98.13% of variance) of the hyperspectral image to extract textural features (contrast, correlation, energy, and homogeneity). The CCR of the model developed using textural variables was less optimistic with a value of 59%. Compared to results of models based on spectral or textural data individually, the performance of the model based on the mixed data of optimal spectral and textural features was the best with an improved CCR of 86%. The results showed that the spectral and textural data of hyperspectral images together can be integrated in order to measure and classify the WHC of fresh chicken breast fillets. [ABSTRACT FROM AUTHOR]

Stingless bee honey produced by Heterotrigona itama from different botanical origins was characterised and discriminated. Three types of stingless bee honey collected from acacia, gelam, and starfruit nectars were analyzed and compared with Apis mellifera honey. The results showed that stingless bee honey samples from the three different botanical origins were significantly different in terms of their moisture content, pH, free acidity, total soluble solids, colour characteristics, sugar content, amino acid content and antioxidant properties. Stingless bee honey was significantly different from Apis mellifera honey in terms of physicochemical and antioxidant properties. The amino acid content was further used in the chemometrics analysis to evaluate the role of amino acid in discriminating honey according to botanical origin. Partial least squares-discriminant analysis (PLS-DA) revealed that the stingless bee honey was completely distinguishable from Apis mellifera honey. Notably, a clear distinction between the stingless bee honey types was also observed. The specific amino acids involved in the distinction of honey were cysteine for acacia and gelam, phenylalanine and 3-hydroxyproline for starfruit, and proline for Apis mellifera honey. The results showed that all honey samples were successfully classified based on amino acid content.

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square−Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness.

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue. [ABSTRACT FROM AUTHOR]

International Olive Oil Council (IOOC) states chemical and organoleptic parameters to classify the commercial grade of olive oil. Finding tools or analytical procedures able to support the organoleptic evaluation would be helpful to streamline and facilitate the commercial classification. The aim of the present study was to evaluate a new tool and validate a procedure that allows a fast and non-invasive volatile compounds detection system, able to assign each sample to its right trade category. Moreover, we tried to test the capability of PTR-ToF-MS in grading olive oils according to their fruity intensity levels. A total of 273 olive oil samples collected from Argentina (21), Chile (10), Italy (191), Morocco (17), Tunisia (4) and EU (30) were analyzed and classified through: (1) Panel Test and (2) PTR-ToF-MS analysis. On the whole PTR-ToF-MS data EVOO and Not EVOO as resulted by Panel Test were clustered by PCA in two main groups and correctly classified by PLS-DA model, confirming the high confidence level (95%) in utilizing analytical spectral data for helping Panel Test and able to easy monitoring the quality formation in the oils, by a fast and cheap control from harvest until the store. The eight protonated masses detected as VIP by the model may be linked to negative olfactory notes. Finally, PCA applied on the volatile profile of 122 classified EVOO highlighted a shift of the samples distribution following the trend of the fruity intensity as assessed by the panelists. In conclusion, this trial confirmed the availability of a new, precise and simple analytical tool as the PTR-ToF-MS, which coupled with an appropriate multivariate data analysis, allows to classify EVOO according to their trade category and fruity intensity. [ABSTRACT FROM AUTHOR]

Objectives: To explore the potential biomarkers for the diagnosis of primary brain stem injury (PBSI) by using metabonomics method to observe the changes of metabolites in rats with PBSI caused death., Methods: PBSI, non-brain stem brain injury and decapitation rat models were established, and metabolic maps of brain stem were obtained by LC-MS metabonomics method and annotated to the HMDB database. Partial least square-discriminant analysis (PLS-DA) and random forest methods were used to screen potential biomarkers associated with PBSI diagnosis., Results: Eighty-six potential metabolic markers associated with PBSI were screened by PLS-DA. They were modeled and predicted by random forest algorithm with an accuracy rate of 83.3%. The 818 metabolic markers annotated to HMDB database were used for random forest modeling and prediction, and the accuracy rate was 88.9%. According to the importance in the identification of cause of death, the most important metabolic markers that were significantly up-regulated in PBSI group were HMDB0038126 (genipinic acid, GA), HMDB0013272 ( N -lauroylglycine), HMDB0005199 [( R )-salsolinol] and HMDB0013645 ( N , N -dimethylsphingosine)., Conclusions: GA, N -lauroylglycine, ( R )-salsolinol and N , N -dimethylsphingosine are expected to be important metabolite indicators in the diagnosis of PBSI caused death, thus providing clues for forensic medicine practice.

The aim here was to explore the potential of visible and near-infrared (Vis/NIR) hyperspectral imaging (400–1000 nm) to classify fresh chicken breast fillets into different water-holding capacity (WHC) groups. Initially, the extracted spectra and image textural features, as well as the mixed data of the two, were used to develop partial least square-discriminant analysis (PLS-DA) classification models. Smoothing, a first derivative process, and principle component analysis (PCA) were carried out sequentially on the mean spectra of all samples to deal with baseline offsets and identify outlier data. Six samples located outside the confidence ellipses of 95% confidence level in the score plot were defined as outliers. A PLS-DA model based on the outlier-free spectra provided a correct classification rate (CCR) value of 78% in the prediction set. Then, seven optimal wavelengths selected using a successive projections algorithm (SPA) were used to develop a simplified PLS-DA model that obtained a slightly reduced CCR with a value of 73%. Moreover, the gray-level co-occurrence matrix (GLCM) was implemented on the first principle component image (with 98.13% of variance) of the hyperspectral image to extract textural features (contrast, correlation, energy, and homogeneity). The CCR of the model developed using textural variables was less optimistic with a value of 59%. Compared to results of models based on spectral or textural data individually, the performance of the model based on the mixed data of optimal spectral and textural features was the best with an improved CCR of 86%. The results showed that the spectral and textural data of hyperspectral images together can be integrated in order to measure and classify the WHC of fresh chicken breast fillets.

Donkey milk (DM) is an emerging foodstuff, gaining increasing attention due to its nutritional properties. As a high value-added product, it can be subjected to adulteration with cheaper milks, e.g., cow milk. The present work addresses the possibility of developing a fast and economic method for the authentication of pure DM using Near Infrared (NIR) Spectroscopy. For this purpose, 147 samples (67 pure DM and 80 mixtures) were analyzed fresh; additionally, all samples underwent successive freezing-thawing cycles. Partial Least Squares-Discriminant Analysis (PLS-DA) was used to differentiate pure and adulterated samples. PLS-DA model built on the fresh milk correctly classified all training and test samples (100% accuracy). When model was applied to the freeze-thawed individuals, it showed high accuracy (79.7% after 3 cycles), suggesting that the spectroscopic signature of adulteration prevails on that of freezing-thawing, though the impact of the latter was proved to be significant by ANOVA-Simultaneous Component Analysis (ASCA). [Display omitted] • Development of NIR and chemometrics-based approach to authenticate donkey milk. • NIR-based method developed is a rapid, cheap, and not-invasive approach. • Freezing/thawing treatment has significant effect on NIR-spectra of donkey milk. • PLS-DA model achieves good accuracy despite freezing-thawing process application. [ABSTRACT FROM AUTHOR]

Cannabis is used to treat various medical conditions, and lines are commonly classified according to their total concentrations of Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Based on their ratio of total THC to total CBD, cannabis cultivars are commonly classified into high-THC, high-CBD, and hybrid classes. While cultivars from the same class have similar compositions of major cannabinoids, their levels of other cannabinoids and their terpene compositions may differ substantially. Therefore, a more comprehensive and accurate classification of medicinal cannabis cultivars, based on a large number of cannabinoids and terpenes is needed. For this purpose, three different chemometric-based classification models were constructed using three sets of chemical profiles. We examined those models to determine which provides the most accurate "chemovar" classification. This was done by analyzing profiles of cannabinoids, terpenes, and the combination of these substances using the partial least square-discriminant analysis multivariate (PLS-DA) technique. The chemical profiles were selected from the three major classes of medicinal cannabis that are most commonly prescribed to patients in Israel: high-THC, high-cannabigerol (CBG), and hybrid. We studied the correlations between cannabinoids and terpenes to identify major bio-indicators representing the plant's terpene and cannabinoid content. All three PLS-DA models provided highly accurate classifications, utilizing six to nine latent variables with an overall accuracy ranging from 2 to 11% CV. The PLS-DA model applied to the combined cannabinoid-and-terpene profile did the best job of differentiating between the chemovars in terms of misclassification error, sensitivity, specificity, and accuracy. The combined cannabinoid-and-terpene PLS-DA profile had cross-validation and prediction misclassification errors of 4% and 0%, respectively. This is the first study to demonstrate the highly accurate classification of samples of medicinal cannabis based on their cannabinoid and terpene profiles, as compared to cannabinoid profiles alone. Furthermore, our correlation analysis indicated that 11 cannabinoids and terpenes might serve as bio-indicators for 32 different active compounds. These findings suggest that the use of multivariate statistics could assist in breeding studies and serve as a tool for minimizing the mislabeling of cannabis inflorescences. [Display omitted] • Classification of major cannabis classes was good for all data sets. • Terpene-cannabinoid–based PLS-DA provided the best chemovar classification. • Eleven potential bio-indicators predicted 32 cannabinoids and terpenes. [ABSTRACT FROM AUTHOR]

Highlights: [•] NMR metabolomics and histology to study acute toxicity of HMTA based metals salt. [•] Results showed disturbed energy metabolism to the greatest extent. [•] Maximum changes associated with liver physiology observed at 24h p.d. [ABSTRACT FROM AUTHOR]

Highlights: [•] Thermal hyperalgesia was reduced in SCI rats consuming O3PUFA-rich diets. [•] O3PUFA-derived ethanolamines are associated with the antihyperalgesic behaviors. [•] Dietary O3PUFAs reduced the levels of pain biomarkers in the injured spinal cord. [Copyright &y& Elsevier]

Abstract: The texture and aroma volatiles of two non-climacteric near-isogenic lines (NILs) of melon (Cucumis melo L.), SC10-2 and SC7-1, containing introgressions of the Korean cultivar ‘Shongwan Charmi’ accession PI161375 (SC) in the Spanish cultivar ‘Piel de Sapo’ (PS) were studied. Data were examined using different supervised and unsupervised univariate and multivariate statistical techniques in order to determine the aroma volatiles most closely associated with differences in melon texture. SC10-2 showed 65% higher flesh firmness, a higher level of pellet juice content and lower flesh juiciness than PS and was harvested at least 7days later. In SC7-1, only the textural trait, whole fruit hardness, was lower (by 34%) than in PS. About eleven quantitative trait loci for aroma volatiles were mapped in linkage group VII and other nineteen in group X. The aroma compounds discriminated SC10-2 from PS better than SC7-1, as revealed by Partial least squares-discriminant analysis and, to a lesser extent, by Random forest analysis. Around seven aroma volatile compounds, mostly ketones, aldehydes and alcohols, consistently lent support to the discrimination irrespective of the methodology used. Higher levels of several aldehydes (e.g. 2,4-dimethylbenzaldehyde, 2-methylpropanal and 2-methylbutanal), not present in PS, discriminated SC10-2 from PS. Also, SC10-2 lacked some ketones (e.g. 1-phenylethanone), alcohols (e.g. 1-phenylethanol) and one acid (e.g. 2-phenylpropanoic acid) compared with higher content in PS. The former aldehydes and other compounds, such as 3-methylsulfanylpropanal, were the volatiles with the closest association with flesh firmness and harvest, and form part of the phenylalanine, leucine, valine, isoleucine or methionine metabolism and the benzenoid biosynthetic pathway. A negative correlation with the former discriminative compounds of SC7-1 and PS and whole fruit hardness was established. SC7-1 stood out mainly for its higher content of one sulfur-derived compound (S-methyl ethanethioate), one acetate ester (benzyl acetate) and two alcohols (octan-1-ol and (2R, 3S)-3-ethylheptan-2-ol) compared with PS, and one ketone (pentane-2,3-dione), which was absent in PS, but none of these were associated with flesh textural traits. [Copyright &y& Elsevier]

Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.

Stingless bee honey produced by Heterotrigona itama from different botanical origins was characterised and discriminated. Three types of stingless bee honey collected from acacia, gelam, and starfruit nectars were analyzed and compared with Apis mellifera honey. The results showed that stingless bee honey samples from the three different botanical origins were significantly different in terms of their moisture content, pH, free acidity, total soluble solids, colour characteristics, sugar content, amino acid content and antioxidant properties. Stingless bee honey was significantly different from Apis mellifera honey in terms of physicochemical and antioxidant properties. The amino acid content was further used in the chemometrics analysis to evaluate the role of amino acid in discriminating honey according to botanical origin. Partial least squares-discriminant analysis (PLS-DA) revealed that the stingless bee honey was completely distinguishable from Apis mellifera honey. Notably, a clear distinction between the stingless bee honey types was also observed. The specific amino acids involved in the distinction of honey were cysteine for acacia and gelam, phenylalanine and 3-hydroxyproline for starfruit, and proline for Apis mellifera honey. The results showed that all honey samples were successfully classified based on amino acid content. [ABSTRACT FROM AUTHOR]

Metabolomics is one of the more recently developed "omics" that measures low molecular weight (typically < 1500 Da) compounds in biological samples. Metabolomics has been widely explored in environmental, clinical, and industrial biotechnology applications. However, its application to the area of food safety has been limited but preliminary work has demonstrated its value. This chapter describes an untargeted (nontargeted) metabolomics workflow using gas chromatography coupled to mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for extraction of polar metabolites from media, analyzing the extracts using GC-MS and, finally, chemometric data analysis using the software "SIMCA" to identify potential pathogen-specific biomarkers.