Partial Least Squares Discriminant Analysis Research Articles

Non-destructive identification of cashmere and wool fibers based on PLS-DA and LDA using NIR spectroscopy

Manual identification of cashmere and wool fibers is often laborious, subjective, and time-consuming due to their extremely similar features. In order to non-destructively and accurately detect these animal fibers, this study proposes a novel detection method based on machine learning algorithms by near-infrared (NIR) spectroscopy. Building upon the preprocessing of NIR spectroscopy data of cashmere and wool fibers, both partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) classifiers are used to distinguish cashmere and wool fibers. First, four data preprocessing methods are applied: mean normalization (MN), z-score standardization (ZSS), mahalanobis distance (MD), and discrete wavelet transform (DWT). Second, following the preprocessing, PLS-DA is used for feature extraction of the spectral data. Finally, based on the criterion of cumulative contribution rate of 80%, determine the number of principal components (PCs) and use the selected PCs as input for LDA. This study compares three feature extraction methods, principal component analysis (PCA), factor analysis, and sparse principal component analysis (SPCA), and two identification models, k-nearest neighbor (KNN) and decision tree (DT). Experimental results indicate that the proposed PLS-DA-LDA model outperforms the other 11 models, offering a new method for the identification of cashmere and wool fibers using NIR spectroscopy.

Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (Pyropia spp.)

Laver (Pyropia spp.) is a major seaweed that is cultivated and consumed globally. Although quality standards for laver products have been established, traditional physicochemical analyses and sensory evaluations have notable drawbacks regarding rapid-quality inspection. Not all relevant physicochemical quality indices, such as texture, are typically evaluated. Therefore, in this study, we investigated the use of hyperspectral imaging to rapidly, accurately, and objectively determine the quality of dried laver. Hyperspectral images of 25 dried laver samples were captured in the short-wave infrared range from 980 to 2576 nm to assess their moisture, protein content, cutting stress, and other key quality indicators. Spectral signatures were analyzed using partial least-squares discriminant analysis (PLS-DA) to correlate the spectral data with three primary quality index values. The performance of PLS-DA was compared with that of the variable importance in projection score and nonlinear regression analysis methods. The comprehensive quality grading model demonstrated accuracies ranging from 96 to 100%, R2 values from 75 to 92%, and root-mean-square errors from 0.14 to 0.25. These results suggest that the PLS-DA regression model shows great potential for the multivariate analysis of hyperspectral images, serving as an effective quality grading system for dried laver.

Complex Microbial Fertilizer Promotes the Growth of Summer-Sown Short-Season-Cultivated Cotton and Increases Cotton Yield in the Yangtze River Basin by Changing the Soil Microbial Community Structure

The summer-sowing short-season cotton cultivation model is an important method for simplified and mechanized cotton planting in the Yangtze River Basin. However, the effects of microbial fertilizers on cotton growth and soil under this model remain unclear. In 2023, we conducted a systematic analysis on the application of microbial fertilizers (compost) at varying levels (CK, MF1, MF2, and MF3) during different growth stages of cotton (bud, flowering, bolling, and boll opening). Results showed that appropriate microbial fertilizer application (MF2 and MF3) enhanced soil bacterial and fungal diversity, enriched beneficial microorganisms (e.g., Acidobacteriota and Candidatus Udaeobacter), improved soil nutrient availability, and increased antioxidant enzyme activity (POD, SOD), while reducing membrane lipid peroxidation (MDA). These effects led to significant improvements in yield traits, such as cotton plant height, number of fruiting branches and bolls, boll weight, and coat weight. The highest microbial fertilizer application level (MF3) resulted in a 54.35% increase in seed yield and a 75.37% increase in lint yield compared to CK. PLS-DA (Partial Least Squares Discriminant Analysis) and multivariate statistical analyses revealed that microbial fertilizer application fine-tuned soil microbial community composition, emphasizing the dynamic balance of the microbial ecosystem. This study provides scientific support for optimizing microbial fertilizer strategies to enhance the yield and quality of summer-sown short-season cotton and promote sustainable agriculture.

Liquid Chromatography-High-Resolution Mass Spectroscopy-Based Metabolomics for Identification Cytotoxic Compounds From Acalypha Indica L. on MCF-7 Breast Cancer Cells as Potential Anticancer Agents.

Acalypha indica is a weed used in traditional cancer treatments. In this study, we present the first attempt to use a metabolomic approach to identify cytotoxic compounds from A. indica extract with potential anticancer properties. The leaves of A. indica were extracted using ethanol, ethyl acetate, chloroform, and n-hexane. The cytotoxic activity against MCF-7 cells was evaluated, revealing 50% inhibition concentration (IC50) values ranging from 51.88 ±7.80 to 226.86 ±13.27µg/mL. Metabolite profiling using liquid chromatography-high-resolution mass spectroscopy identified 27 metabolites and principal component analysis successfully differentiated the extracts, indicating variability in the compounds extracted using each solvent. To identify the cytotoxic compounds, orthogonal partial least-squares discriminant analysis was used to correlate metabolite profiles with IC50 values from the cytotoxic assay. From the results obtained, we successfully predicted six compounds contributing to the anticancer activity of A. indica, namely hernanol, 13(S)-HpOTrE, (±)9-HpODE, (+)-catechin, traumatic acid, and one compound as assumed to be theobromine. In silico analysis predicted that (+)-catechin and hernanol bind to the alpha-estrogen receptor with an affinity similar to that of doxorubicin.

Geographical Origin Traceability of Navel Oranges Based on Near-Infrared Spectroscopy Combined with Deep Learning

The quality and price of navel oranges vary depending on their geographical origin, thus providing a financial incentive for origin fraud. To prevent this phenomenon, it is necessary to explore a fast, non-destructive, and precise method for tracing the origin of navel oranges. In this study, a total of 490 Newhall navel oranges were selected from five major production regions in China, and the diffuse reflectance near-infrared spectrum in 4000–10,000 cm−1 were non-invasively collected. We examined seven preprocessing techniques for the spectra, including Savitzky–Golay (SG) smoothing, first derivative (FD), multiplicative scattering correction (MSC), combinations of SG with MSC (SG+MSC), SG with FD (SG+FD), MSC with FD (MSC+FD), and three combined (SG+MSC+FD). A one-dimensional convolutional neural network (1DCNN) deep learning model for geographical origin tracing of navel orange was established, and five machine learning algorithms, i.e., partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and back-propagation neural network (BPNN), were compared with 1DCNN. The results show that the 1DCNN model based on the SG+FD preprocessing method achieved the optimal performance for the testing set, with prediction accuracy, precision, recall, and F1-score of 97.92%, 98%, 97.95%, and 97.90%, respectively. Therefore, NIRS combined with deep learning has a significant research and application value in the rapid, nondestructive, and accurate geographical origin traceability of agricultural products.

Can chick meconium serve as a source of biomarkers linked to hatchling quality and the age of parent stock?

One possible approach to selecting chicks based on quality involves identifying biomarkers in biological samples. Concurrently, understanding the metabolic profile of chicks from different-aged breeders is essential for developing strategies to mitigate the age-related effects on hatchability. This study investigated whether chick quality and breeder age influence the metabolic profile of layer chick meconium. A total of 143 chicks from laying breeder hens, categorized as young, middle-aged or old, were visually assessed for quality, and meconium samples were collected for metabolomic analysis. Sixteen metabolites were found to be associated with good-quality chicks across all breeder ages, with an overall accuracy of 81.94 %. Using metabolite profiles, the accuracy for predicting young-hen-chick quality was 93.2 %, with high sensitivity (92.30 %) and specificity (93.75 %). Four metabolites were associated with poor-quality chicks with an overall accuracy of 77.53 %. Partial least squares discriminant analysis (PLS-DA) revealed enhanced metabolite separation in good-quality chicks, with five metabolites exhibiting high area under the curve (AUC) values (>90 %) in chicks from young hens compared to chicks from hens of other ages, including metabolites related to energy metabolism, hormonal activity, vitamin D synthesis and peptide constitution. Disregarding maternal age, five metabolites varied between good- and poor-quality chicks, but with a low accuracy of 61.26 % for quality discrimination. Chicks from young, middle-aged, and old hens exhibited 12, 11, and 2 metabolites that varied the expression between good and poor qualities, with accuracies for predicting good-quality chicks of 74.46 %, 70.83 %, and 51.06 %, respectively. Certain metabolites with 70 < AUC < 80 % have emerged as potential biomarkers for distinguishing between good- and poor-quality layer chicks. These include metabolites related to energy and growth metabolism, tryptophan and methionine metabolism, antioxidants and some with no known function in embryos. This work identified potential metabolites that can be investigated to mitigate the effects of hen age on hatchability. Additionally, several metabolites have emerged as potential biomarkers for distinguishing between good- and poor-quality chicks, depending on the breeder's age.

Application of ATR-FTIR spectroscopy and multivariate statistical analysis in cancer diagnosis.

Lung cancer is one of the most prevalent and lethal malignant tumors worldwide. Currently, clinical diagnosis primarily relies on chest X-ray examinations, histopathological analysis, and the detection of tumor markers in blood. However, each of these methods has inherent limitations. The current study aims to explore novel diagnostic approaches for lung cancer by employing attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy in conjunction with multiple machine learning models. Fourier transform infrared spectroscopy can detect subtle differences in the material structures that reflect the carcinogenic process between lung cancer tissues and normal tissues. By applying principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to analyze infrared spectral data, these subtle differences can be amplified. The study revealed that the combination of spectral bands within the 3500-3000 cm-1 and 1600-1500 cm-1 ranges is particularly significant for differentiating between the two groups. Three classification models-Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Linear Discriminant Analysis (LDA)-were constructed for spectral analysis of various band combinations. The results indicated that in detecting lung cancer samples, the combination of the 3500-3000 cm-1 and 1600-1500 cm-1 bands offers significant advantages. The analysis of the receiver operating characteristic (ROC) curve demonstrated that the area under the curve (AUC) exceeded 0.95 for all models, with the LDA model achieving an accuracy rate of 99.4% in identifying lung cancer patients compared to healthy individuals. The findings suggest that the integration of ATR-FTIR spectroscopy with multiple machine learning models represents a promising auxiliary diagnostic method for clinical lung cancer diagnosis, enabling detection at the molecular level.

Analysis of volatile compounds and vintage discrimination of raw Pu-erh tea based on GC-IMS and GC-MS combined with data fusion.

Storage duration significantly influences the aroma profile of raw Pu-erh tea. To comprehensively investigate the differences in the volatile compounds across various vintages of raw Pu-erh teas and achieve the rapid classification of tea vintages, volatile compounds of raw Pu-erh tea with different years (2020-2023) were analyzed using a combination of gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-mass spectrometry (GC-MS). The datasets obtained from both techniques were integrated through low-level and mid-level data fusion strategies. Additionally, partial least squares discriminant analysis (PLS-DA) and random forest (RF) machine learning algorithms were applied to develop predictive models for the classification of tea storage durations. Consequently, GC-IMS and GC-MS identified 54 and 76 volatile compounds, respectively. Notably, the RF model, particularly when coupled with mid-level data fusion, exhibited exceptional predictive accuracy for tea storage time, reaching an accuracy of 100%. These findings provide a reference for elucidating the aroma characteristics of raw Pu-erh tea of different vintages and demonstrate that data fusion combined with machine learning has great potential for ensuring food quality.

Exploring the Mechanism of Action and Potential Targets of Saorilao-4 Decoction in the Treatment of Pulmonary Fibrosis in Rats by Metabolomics.

Pulmonary fibrosis (PF) is a chronic progressive disease marked by alveolar epithelial cell damage. Saorilao-4 decoction (SRL), a traditional Mongolian prescription, has demonstrated therapeutic effects on PF, though its mechanism of action remains elusive. This study used a bleomycin-induced fibrosis rat model to evaluate SRL's effects by measuring inflammatory factors, assessing fibrosis-related indices, and performing histopathological lung examinations. Serum metabolite levels in the experimental groups were measured using high-performance liquid chromatography coupled with mass spectrometry. Data analysis involved principal component and partial least-squares discriminant analyses, followed by functional enrichment analysis of differential metabolites. SRL significantly ameliorated alveolar interstitial injury, fibrosis, and metabolic disorders induced by bleomycin. Additionally, we identified 71 metabolic components related to PF progression, including sphingolipids and fatty acids. Administration of SRL affected 59 metabolic components involved in purine, cysteine and methionine, and arginine and proline metabolisms. Specifically, SRL regulated the levels of hexadecanoic acid, S-adenosylmethionine, 3-oxopalmitoyl coenzyme A, and dodecanoic acid metabolites, thereby improving the metabolic course of PF. In conclusion, this study offers insights into the potential mechanisms of SRL in treating PF from a metabolomics perspective. It provides valuable information for its clinical application.

Non-targeted metabolomics reveals fatty acid and associated pathways driving resistance to whitefly and tomato leafminer in wild tomato accessions.

Wild tomato species exhibit natural insect resistance, yet the specific secondary metabolites and underlying mechanisms governing the resistance remain unclear. Moreover, defense expression dynamically adapts to insect herbivory, causing significant metabolic changes and species-specific secondary metabolite accumulation. The present study aims to identify the resistance-related metabolites in wild tomato accessions that influence the defense mechanism against whitefly (Bemisia tabaci Asia II 7) and leafminer (Phthorimaea absoluta). In this study, LC-HRMS-based non-targeted metabolomics of resistant wild (Solanum cheesmaniae and Solanum galapagense) and susceptible cultivated (Solanum lycopersicum) accessions following 6- and 12-h post-infestation (hpi) by B. tabaci Asia II 7 and P. absoluta revealed distinct sets of resistance-related constitutive (RRC) and induced (RRI) metabolites. The key resistance-related metabolites were those involved in the fatty acid and associated biosynthesis pathways (e.g., triacontane, di-heptanoic acid, dodecanoic acid, undecanoic acid, N-hexadecanoic acid, pentacosane, monogalactosyldiacylglycerols, sphinganine, and 12-hydroxyjasmonic acid), which are recognized for their direct or indirect role in mediating plant defense against insects. Additionally, the differential accumulation of metabolites was evident through partial least squares-discriminant analysis (PLS-DA), highlighting differences in metabolite profiles between resistant and susceptible accessions at 6 and 12 hpi of B. tabaci and P. absoluta. Volcano plot analysis revealed a higher number of significantly upregulated metabolites in wild accessions following herbivory. Moreover, wild tomato accessions responded uniquely to B. tabaci and P. absoluta, highlighting species-specific metabolic responses of tomato accessions to the two feeding guilds. This study uncovered biochemical mechanisms governing resistance in wild tomato accessions, elucidated the influence of dual herbivory on the plant metabolome, and offered well-characterized parent materials and candidate metabolites for breeding insect-resistant varieties.

Near-Infrared Spectroscopy as a Tool for the Traceability Control of High-Quality Iberian Dry-Cured Meat Products

Near-infrared spectroscopy (NIRS) was evaluated to trace the high hydrostatic pressure (HHP) processing and preservation temperature (4 °C vs. 20 °C) over the course of a long term in vacuum-packaged Iberian dry-cured tenderloin (Iliopsoas et psoas minor). Spectra were obtained from a total of 298 samples, without opening the package, using a handheld MicroNIRTM 1700 OnSite-W microspectrophotometer (908.1 nm–1676.2 nm) (VIAVI Solutions Inc., United States). The discriminant models were developed by means of partial least squares-discriminant analysis (PLS-DA). The models obtained were capable of correctly classifying more than 60% of the samples according to their HHP processing, while almost 100% of the samples were correctly classified according to the temperature at which the samples were preserved. Thus, NIRS could help to support the traceability of treatments that represent a high added value to the product, such as HHP in premium Iberian dry-cured products.

Short-Wave Infrared Spectroscopy for On-Site Discrimination of Hazardous Mineral Fibers Using Machine Learning Techniques

Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best (RecallM and AccuracyM equal to 1.00), followed by PCA-KNN (RecallM of 0.98–1.00 and AccuracyM equal to 1.00). Poorer performances were obtained by PLS-DA (RecallM of 0.68–0.72 and AccuracyM equal to 0.95) and PCA-DA (RecallM of 0.66–0.70 and AccuracyM equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies.

Feasibility study on fingerprinting organic and conventional mango fruits, chips, and juice using portable near-infrared spectroscopy.

This research examined the distinction between organic and conventional mango fruits, chips, and juice using portable near-infrared (NIR) spectroscopy. A comprehensive analysis was conducted on a sample of 100 mangoes (comprising 50 organic and 50 conventional) utilising a portable NIR spectrometer that spans a wavelength range from 900 to 1700 nm. The mangoes were assessed in their entirety and their juice and chip forms. The spectral data underwent pre-processing through methodologies such as multiplicative scatter correction (MSC), standard normal variate (SNV), and derivatives to enhance the precision of the models. Principal component analysis (PCA) and various multivariate classification algorithms, including linear discriminant analysis (LDA), random forest (RF), k-nearest neighbors (kNN), and partial least squares discriminant analysis (PLSDA), were utilised to categorise the samples effectively. The findings indicated that the random forest method and specific pre-processing techniques achieved the highest classification accuracy for distinguishing organic and conventional mango products. For mango fruit and chips, it achieved 88.76% and 77.98% accuracy, respectively, when pre-processed using the second derivative, while for juice, it achieved 87.53% accuracy without pre-processing. This investigation demonstrates the efficacy of portable NIR spectroscopy as a dependable and non-invasive method for verifying organic mango products, thereby enhancing the integrity of food labelling and fostering consumer confidence.

Analyzing the effect of different storage times and different processes on the volatile components in Shenling Baizhu powder by HS-GC-IMS combined with multivariate statistics.

this study aimed to examine the effect of different storage times (0, 7, 24, 57, and 119 days) on the volatile components of Shenling Baizhu powder across different preparation processes (Pharmacopoeia, ultra-micro pulverization-pulverization, and microparticle design methods). The findings offer insights to guide quality control measures for Shenling Baizhu powder. gas chromatography-ion mobility spectrometry (GC-IMS) was employed to ascertain the volatile components in Shenling Baizhu powder at various storage times across different preparation processes. In addition, the study compared the dynamic changes of these components under different conditions. Principal component analysis (PCA) and orthogonal partial least-squares discrimination analysis (OPLS-DA) were conducted using SIMCA-P19.0 software to analyze differences among these volatile components. the volatile components identified by GC-IMS comprised 52 monomers and dimers and polymers of some substances, primarily including aldehydes, esters, terpenes, nitrogenous compounds, and ketones. Fingerprint spectra revealed significant differences in the types and quantities of volatile components in Shenling Baizhu powder at different storage times under different processes. PCA indicated negligible differences in the types of volatile components among samples prepared using different processes. An OPLS-DA model was developed to distinguish storage times and identify characteristic markers. In addition, 22 volatile components with variable importance for the projection (VIP) > 1 were identified, significantly contributing to the discrimination of storage times for Shenling Baizhu powder. Overall, the content of volatile components in the samples gradually decreased with prolonged storage time. Samples prepared using the Pharmacopoeia method exhibited the most significant decline, whereas those prepared using the microparticle design method showed the least reduction in volatile components. HS-GC-IMS effectively captures the changing trends of volatile components in Shenling Baizhu powder across different preparation processes and storage times. This analysis provides a reference for quality control and process optimization in the production of Shenling Baizhu powder.

DOP119 Predicting response to anti-tumour necrosis factor-alpha therapy-α in fibrostenotic Crohn’s disease using infra-red microspectroscopy

Abstract Background Fibrostenotic strictures are common in Crohn’s disease, have limited treatment options and lack validated biomarkers to predict treatment response. Fourier-transform infra-red (FTIR) spectroscopy provides reproducible biochemical information from biospecimens using a rapid, label-free, non-destructive technique via molecular vibrations. We previously completed a proof-of-concept study that identified spectral biomarkers for inflammation in a model of colitis.1 The aim of this study was to investigate whether the tissue biochemical "fingerprint" using FTIR spectroscopy can predict patient clinical response to anti-tumour necrosis-factor-α (anti-TNFα) therapy, and identify spectral biomarkers for treatment response. Methods Hyperspectral images were acquired using the Agilent Cary 670 FPA-IR coupled with the Agilent Cary 620 microscope in transflection mode in the wavenumber region 1800 – 800 cm-1 from fixed ileo-colonoscopic biopsies from 62 patients with Crohn’s disease and 12 healthy controls. A subset of patients (n = 24) were included from STRIDENT2, a randomised study assessing adalimumab therapy for intestinal Crohn’s strictures. Thousands of spectra were obtained per sample, pre-processed via transformation to the second derivative, normalised and reduced to 25 spectra per sample prior to analysis using partial least squares discriminant analysis (PLSDA) with internal cross-validation. Spectral biomarkers were identified by the most contributive infra-red bands from the latent variables (LV) and significant variable importance in projection (VIP) scores of &amp;gt;1. Adjacent histological sections were stained with H&amp;E, Masson’s trichrome and α-smooth muscle actin with inflammation and fibrosis scored by a clinical pathologist. Results Spectra from baseline biopsies from the 24 patients in the STRIDENT study demonstrated excellent performance using PLSDA in discriminating between responders and non-responders in relation to the 12 month clinical outcomes, defined by area under the receiver operating characteristic curve (AUC) &amp;gt;0.9 for MRI and IUS responses, normalisation of faecal calprotectin and CRP, CDAI and pain responses (Table 1). Spectral biomarkers of interest for stricture response to anti-TNFα are shown in Figure 1 and include the amide I and II protein bands (peaks at 1651 and 1543 cm-1, respectively), the carboxylate group of proteins (1454 cm-1), lipid bands (1750 and 1395 cm-1), collagen band (1236 cm-1), glycoprotein bands (1081 and 1030 cm-1), and nucleic acid bands (1236, 1081 and 966 cm-1). Conclusion The novel technique of FTIR spectroscopy detects tissue biochemical "fingerprints" in fibrostenotic Crohn’s disease that demonstrate excellent discrimination for predicting clinical response to adalimumab therapy.

P0089 Infra-red microspectroscopy provides quantitative assessment of fibro-inflammation in Crohn’s disease strictures and predicts post-operative recurrence

Abstract Background Quantitative assessment of fibro-inflammation in Crohn’s disease strictures remains a challenge despite its role in clinical decision making using immunomodulatory drugs or endoscopic/surgical interventions. While histology remains the gold standard, no validated histological scoring systems for Crohn’s disease strictures exist and assessment is subject to significant intra- and inter-observer variability. Fourier-transform infra-red (FTIR) microspectroscopy is a reproducible, label-free and non-destructive approach for analysing tissue biochemistry via molecular vibrations. We have previously shown accurate assessment of inflammation severity using this technique in colitis models (1). The aim of this study was to assess Crohn’s disease strictures using FTIR spectroscopy and to evaluate if tissue spectra can predict post-operative Crohn’s disease recurrence. Methods Full-thickness Crohn’s stricture resections (n=72) were obtained in paraffin and sectioned for hyperspectral imaging using the Agilent Cary 670 FPA-IR coupled with the Agilent Cary 620 microscope in transflection mode in the wavenumber region 1800–800 cm-1. Nearly 8 million spectra in total were acquired from regions of interest in each of the bowel wall layers per sample (Figure 1). Spectra was transformed to the second derivative, normalised and reduced to 25 spectra per sample prior to analysis using partial least squares discriminant analysis (PLSDA) with internal cross-validation. Adjacent tissue sections were stained with H&amp;E, Masson’s trichrome and α-smooth muscle actin. Histology was scored based on the averaged scores of three independent clinical histopathologists for inflammation, fibrosis and muscular changes. Results Tissue infra-red spectra demonstrated excellent performance defined by area under the receiver operating characteristic curve (AUC) of ≥0.9 using PLSDA to classify severe from mild inflammation and good (AUC ≥0.8) to excellent (AUC ≥0.9) performance to classify severe from mild fibrosis and muscular hyperplasia/hypertrophy. In those with complete macroscopic Crohn’s disease resection (n=51), spectra from index stricture resections demonstrated good performance (AUC ≥0.8) in discriminating between future significant endoscopic recurrence defined by Rutgeert’s score ≥ i2 (2) and excellent performance for surgical recurrence (AUC ≥0.9) defined as future surgical resection of Crohn’s recurrence. Conclusion FTIR microspectroscopy can detect biochemical differences underlying inflammation, fibrosis and muscular changes in Crohn’s strictures and provide simultaneous quantitative assessment. The biochemical tissue fingerprints demonstrate good to excellent discrimination for predicting endoscopic and surgical Crohn’s recurrence, respectively.

The Dynamic Changes in Volatile Compounds During Wuyi Rock Tea (WRT) Processing: More than a Contribution to Aroma Quality

Wuyi Rock tea (WRT), originating from the northern region of Fujian province, has a good reputation for its distinctive Yan flavor and floral–fruity aroma. The aroma quality, an essential element of the Yan flavor, undergoes various changes during the manufacturing process of WRT. To enhance the understanding of the formation patterns of WRT aroma and its influence on the flavor quality of WRT, we utilized both manufactured WRT (Rougui tea) and primary tea as materials. Utilizing a sensory evaluation, detection of volatile compounds, and multivariate statistical analysis, we identified and characterized the distinctive volatile components present in WRT. The sensory evaluation and radar chart analysis revealed that the primary tea exhibited a strong and lasting aroma, along with a mellow taste and a prominent Yan flavor. Through gas chromatography time-of-flight mass spectrometry (GC-TOF MS), a total of 251 volatile compounds were identified. The odor activity value (OAV) was calculated to identify key aroma-active compounds in the primary tea. The results indicated that a total of 14 compounds had an OAV greater than 1.0, including (2-nitroethyl) benzene, indole, and geranylacetone. These compounds exhibited floral and fruity aroma attributes. They primarily formed and accumulated during the latter stages of WRT. Using a partial least squares discrimination analysis (PLS-DA) combined with a variable importance in projection (VIP) score greater than 1.0 as a criterion, a total of 89 compounds were identified. Furthermore, out of the selected compounds, 15 types, including geraniol, 1-nonanol, and 1-butyl-2-ethyl-cyclopropene, were found to exclusively exist during the enzymatic manufacturing stages, particularly during the intermediate and later phases of the turn-over process (the last-three-times turn-over treatments), exhibiting predominantly floral and sweet fragrances. In contrast, during the non-enzymatic stages, only four compounds, such as pentanoic acid and phenylmethyl ester, were detected, exhibiting a fruity aroma profile. These volatile compounds significantly influenced the quality attributes of the final tea product, resulting in strong and lasting characteristics, particularly marked by a pronounced floral and fruity aroma. This study revealed how the aroma quality in WRT is developed and pinpointed possible volatile compounds that react to post-harvest treatments, thereby offering valuable insights relating to the intelligent production strategies of WRT.

Rapid identification of the volatile markers in lotus seed using headspace-gas chromatography ion-mobility spectrometry

To rapidly identify the volatile markers in lotus seeds, this research compared the volatile organic compounds (VOCs) of five lotus seed samples using gas chromatography-ion mobility spectroscopy (GC-IMS) and chemometric analysis. The results revealed that 49 VOCs were identified from the lotus seed samples, including 16 aldehydes, 15 alcohols, 8 ketones, 3 esters, 3 acids, 3 terpenes, and 1 heterocyclic compound, respectively. Among these, 1-pentanol M, 2-methylbutan-1-ol M, 2-methylbutan-1-ol D, and 1-hexanol M were identified as the volatile markers. Based on the VOC analysis using GC-IMS, effective differentiation of lotus seeds was achieved through chemometric techniques, such as principal component analysis (PCA), cluster analysis (CA), and partial least squares discriminant analysis (PLS-DA). 2-furaldehyde D, hexanoic acid D, and 1-propanol M were found to be the most important components contributing to the differences among the five lotus seed samples. This research demonstrates that GC-IMS coupled with chemometric analysis provides valuable reference information for the identification and authenticity evaluation of lotus seeds, helping ensure their quality on the market and offering theoretical support for their identification and quality assessment.

Examination of SWIR hyperspectral imaging for estimating corewood – outerwood transition age for Douglas-fir grown at different planting densities

Abstract Silvicultural practices, particularly planting density (PD), significantly affect the wood quality of plantation-grown trees by influencing the age of transition from corewood to outerwood. Determining transition age (TA) is challenging. Near infrared hyperspectral imaging was used to assess the age of transition from corewood to outerwood in Douglas-fir (Pseudotsuga menziesii) grown under different PD. Wood samples were taken from permanent research plots with six PD ranging from 2,990 to 248 stems/hectare. Twenty trees were destructively sampled at age 25, and wood discs were collected at 10 % of total tree height. Pith-to-bark radial strips obtained from each disc were scanned using a compovision hyperspectral imaging system (1,002–2,350 nm). Partial least square discriminant analysis (PLS-DA) models were fitted to approximate TA using hyperspectral data representative of corewood and outerwood. PD influenced TA and the duration over which it occurred. Average age of transition was estimated to vary from 10 (closest spacing) to 14 years (widest). Closely spaced trees exhibited an abrupt transition, while widely spaced trees had a more gradual transition. However, PLS-DA likely underestimated the transition duration for widely spaced trees, as their average ring densities did not yet match those typical of outerwood based on X-ray densitometry data.

Authentication of Java Turmeric (Curcuma xanthorrhiza) from Turmeric (Curcuma longa) Using a Combination of UV-VIS-IR Spectrum and Chemometrics

Java turmeric (Curcuma xanthorriza) and turmeric (Curcuma longa) show similar colors, so they have the potential to be adulterated with each other, especially if they are presented in powder. This research aims to develop an analytical method for authenticating both types of samples with adulterant concentrations of 0.01% w/w and 0.005% w/w for the infrared range and 0.5 μg/g and 1 μg/g for the UV-Vis range. The pure sample was extracted for 40 minutes with 1:10 ethanol using ultrasonication. The extract was then concentrated using a rotary evaporator and freeze dryer. Adulterant samples were prepared by mixing both types of extracts. The absorption of the solution was measured at a wavelength of 200–800 nm and a wave number of 4000–400 cm-1. Multivariate analysis using partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA) was performed on the spectra. PLS-DA has not been able to authenticate adulterated samples. However, SIMCA analysis can detect differences between pure curcuma and adulterated samples in the infrared range until a concentration of 0.005% w/w, while it can only authenticate correctly in the UV-Vis range until a 1 μg/g concentration.