Partial Least Squares Discriminant Analysis Model Research Articles

Deep learning algorithm development for early detection of Botrytis cinerea infected strawberry fruit using hyperspectral fluorescence imaging

Botrytis cinerea is a strawberry disease that causes economic loss worldwide. If a disease outbreak occurs during storage or transportation, it can spread rapidly to neighboring objects; thus, there is a need to develop early diagnostic techniques to prevent it. In this study, we developed a method to rapidly and nondestructively determine the infection stage in strawberry fruit using hyperspectral fluorescence imaging. ‘Keumsil’ cultivar strawberries were used, and hyperspectral fluorescence images were acquired over 144 h in control and inoculation groups. Strawberries were categorized into four infection stages based on visible mold spores: healthy, asymptomatic, infected, and after-infected. Hyperspectral fluorescence spectra were extracted to develop a one-dimensional convolutional neural network (1D-CNN) model based on partial least squares-discriminant analysis (PLS-DA), VGG-19, and ResNet-50; data augmentation techniques and six spectral preprocessing techniques were applied to the datasets. The application of data augmentation techniques improved the performances of the PLS-DA and 1D-CNN models in determining the infection stage. The performance of the ResNet-50-based 1D-CNN model with mean normalization data and data augmentation technique was the best, with 96.88% precision, 96.87% recall, 96.85% F1-score, and 96.86% accuracy. The results of this study showed that it is possible to determine the infection stage of Botrytis cinerea on strawberry fruit using hyperspectral fluorescence imaging and 1D-CNN techniques. This technology is expected to be applied for the early detection of Botrytis cinerea in strawberry growth, postharvest sorting and packing, and distribution stages.

Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

Kerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq; therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q2 cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines.

Detection and diagnosis of bacterial pathogens in urine using laser-induced breakdown spectroscopy

The presence of bacterial cells from three species has been detected in clinical specimens of human urine using laser-induced breakdown spectroscopy (LIBS) by using a partial least squares discriminant analysis (PLS-DA) of 360 spectra obtained from 12 specimens of infected urine and 239 spectra obtained from eight specimens of sterile urine. Nominally sterile urine specimens obtained from four patients at a local hospital after being screened negative for the presence of bacterial pathogens were spiked with known aliquots of Escherichia coli, Staphylococcus aureus, and Enterobacter cloacae to simulate clinical urinary tract infections. Fifteen emission line intensities measured from the LIBS spectra and 92 ratios of those line intensities were used as 107 independent variables in the PLS-DA for discrimination between bacteria-containing specimens and sterile specimens. The PLS-DA models possessed a 98.3% sensitivity and a 97.9% specificity for the detection of pathogenic cells in urine when single-shot LIBS spectra were tested. To increase the signal to noise ratio, thirty spectra acquired from a single specimen were also averaged together and the averaged spectra were used to construct a model. When each averaged spectrum was withheld from the model individually for testing, the diagnostic test possessed a 100% sensitivity and a 100% specificity for the detection of bacterial cells in urine, although the number of test spectra was necessarily reduced.The entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected. This PCA-ANN test possessed an overall sensitivity of 97.2%, an overall specificity of 98.6%, and an overall classification accuracy of 97.9% when using 80% of the data to build a model and withholding 20% for cross-validation testing. The PCA-ANN was also performed on each of the 12 bacteria-containing filters individually, using the other 11 filters to build the model. The average sensitivity of this test, calculated by averaging the sensitivities measured for each of the three bacterial species, was 70.9% and the average specificity was 85.5%. Based on these results, the average classification accuracy for the test when used to discriminate between the three microorganisms was 80.6%. These results indicate the potential usefulness of LIBS for rapidly detecting and possibly diagnosing urinary tract infections in a clinical setting.

The ice recrystallization inhibition activity of wheat glutenin hydrolysates and effect of salt on their activity

This study aimed to investigate the antifreeze activity of wheat glutenin hydrolysates produced by Alcalase and trypsin at varying degrees of hydrolysis. The influence of salt concentrations in two dispersing media, phosphate-buffered saline (PBS) and NaCl solution, and the types of cations, NaCl and CaCl2, on hydrolysates was further analyzed. The results revealed a consistently higher ice recrystallization inhibition (IRI) activity for all hydrolysates dispersed in 0.1 x PBS than in 1 x PBS. Interestingly, glutenin hydrolysates exhibited better IRI activity in CaCl2 than in NaCl solution under the same ionic strength. Furthermore, trypsin-derived hydrolysates (with a molar mass about 10 kDa) consistently had better IRI activity than those produced by Alcalase (with a molar mass less than 1 kDa), regardless of the dispersing media. The observed increase in IRI activity was associated with alterations in the secondary structure, characterized by a higher α-helix and lower β-turns content. This suggests that structural rigidity of the molecules plays a significant role in enhancing the antifreeze activity of peptides. Additionally, the scores plot of the partial least-squares discriminant analysis model indicates that salt concentration had a more significant impact than salt type on the IRI activity of glutenin hydrolysates. In conclusion, our findings provide insights into the factors influencing the IRI activity of protein hydrolysates.

Validation of Raman spectroscopic models to verify the origin of Australian beef grown under different production systems

Verification of beef production systems and authentication of origin is becoming increasingly important as consumers base purchase decisions on a greater number of perceived values including the healthiness and environmental impact of products. Previously Raman spectroscopy has been explored as a tool to classify carcases from grass and grain fed cattle. Thus, the aim of the current study was to validate Partial Least Squares Discriminant Analysis (PLS-DA) models created using independent samples from carcases sampled from northern and southern Australian production systems in 2019, 2020 and 2021. Validation of the robustness of discrimination models was undertaken using spectral measures of fat from 585 carcases which were measured in 2022 using a Raman handheld device with a sample excised for fatty acid analysis. PLS-DA models were constructed and then employed to classify samples as either grass or grain fed in a two-class model. Overall, predictions were high with accuracies of up to 95.7% however, variation in the predictive ability was noted with models created for southern cattle yielding an accuracy of 73.2%. While some variation in fatty acids and therefore models can be attributed to differences in genetics, management and diet, the impact of duration of feeding is currently unknown and thus further work is warranted.

Nondestructive assessment of maturity in cantaloupe using Raman spectroscopy with carotenoids as biomarkers

Currently, there is no reliable, non-destructive way to assess the maturity of cantaloupes (Cucumis melo L.). Here, we investigated the extent to which Raman spectroscopy can be used to determine cantaloupe maturity. Five cantaloupe cultivars were grown and harvested at 13, 26, and 39 days after anthesis. Raman spectra from cantaloupes were acquired and partial least-square discriminant analysis (PLS-DA) was used to predict maturity based on the collected spectra. The PLS-DA model predicted maturity with up to 100 % accuracy in the cultivars studied. HPLC analysis of lutein and β-carotene in cantaloupe rind showed an increase in the concentration of carotenoids with maturity. The same trend was observed in the vibrational bands originating from carotenoids in the acquired Raman spectra. Based on this, Raman spectroscopy can primarily detect the changes of carotenoids in different cultivars of cantaloupe rind, which can be used for non-invasive and non-destructive assessment of fruit maturity.

Raman Spectra of Blood Serum as Holistic Biomarker for Differential Auxiliary Diagnoses of Attention Deficit and Hyperactivity Disorder (ADHD) in Adults

Attention deficit and hyperactivity disorder (ADHD) is a prevalent neurodevelopmental condition, impacting approximately 10% of children globally. A significant proportion, around 30–50%, of those diagnosed during childhood continue to manifest ADHD symptoms into adulthood, with 2–5% of adults experiencing the condition. The existing diagnostic framework for ADHD relies on clinical assessments and interviews conducted by healthcare professionals. This diagnostic process is complicated by the disorder’s overlap in symptoms and frequent comorbidities with other neurodevelopmental conditions, particularly bipolar disorder during its manic phase, adding complexity to achieving accurate and timely diagnoses. Despite extensive efforts to identify reliable biomarkers that could enhance the clinical diagnosis, this objective remains elusive. In this study, Raman spectroscopy, combined with multivariate statistical methods, was employed to construct a model based on the analysis of blood serum samples. The developed partial least-squares discriminant analysis (PLS-DA) model demonstrated an ability to differentiate between individuals with ADHD, healthy individuals, and those diagnosed with bipolar disorder in the manic phase, with a total accuracy of 97.4%. The innovative approach in this model involves utilizing the entire Raman spectrum, within the 450–1720 cm−1 range, as a comprehensive representation of the biochemical blood serum setting, thus serving as a holistic spectroscopic biomarker. This method circumvents the necessity to pinpoint specific chemical substances associated with the disorders, eliminating the reliance on specific molecular biomarkers. Moreover, the developed model relies on a sensitive and reliable technique that is cost-effective and rapid, presenting itself as a promising complementary diagnostic tool for clinical settings. The potential for Raman spectroscopy to contribute to the diagnostic process suggests a step forward in addressing the challenges associated with accurately identifying and distinguishing ADHD from other related conditions.

Characterization of volatile compounds from healthy and citrus black spot-infected Valencia orange juice and essential oil by using gas chromatography–mass spectrometry

Citrus black spot (Phyllosticta citricarpa, CBS) is an important fungal disease that causes rind blemishes and affects quality of citrus fruits. The response of citrus to CBS in terms of volatiles was evaluated using molecular sensory science approaches. Fifty and twenty-one volatiles were identified in the orange juice and essential oil samples, respectively, via gas chromatography–mass spectrometry (GC–MS). The total volatile content in the samples increased after CBS infection, especially in the severe-infection group (SEG) juice and moderate-infection group (MOG) essential oil, which reached the highest levels. CBS enhanced floral, fruity, and off-flavor aromas and decreased the green aroma in citrus juice. Citrusy, floral, and green aromas increased in the CBS-infected essential oil. Six/five potential markers were screened in citrus juice/essential oil, respectively using the orthogonal partial least-square discriminant analysis (OPLS-DA) model. The changes in aroma profile and the difference in infection levels in citrus were attributed to these odorants.

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

While crushed pinecone mulch holds promise as a beneficial material for blueberry cultivation, research on its effectiveness remains limited. Crop leaf characteristics can serve as parameters for assessing mulching effects, although there are several limitations, including the need to analyze various distinct characteristics separately. The combination of hyperspectral data and machine learning techniques is expected to enable the selection of only the most important features among these characteristics. In this study, we investigated the impact of various mulching treatments utilizing pine tree byproducts, including crushed pinecones. Mulching variations included non-mulching (NM), crushed pinecones (PCs), a mixture of crushed pinecones and sulfur (PCS), pine needles (PNs), and sulfur treatment (S). Conventional methods were employed to measure leaf growth (length and width) and physiological characteristics (chlorophyll content, chlorophyll fluorescence, and stomatal conductance). Hyperspectral reflectance was also measured, and classification models using Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting (XGBoost) were developed for crop characteristics, vegetation indices (VIs), visible and near-infrared (VNIR), and short-wave infrared (SWIR). The results showed that using crushed pinecones as the sole mulching material for blueberries, without sulfur treatment, had a positive impact on blueberry growth. The PC treatment exhibited a dual effect on plant growth by lowering the soil pH to 5.89 and maintaining soil moisture within the range of 26.33–35.20%. We observed distinct differences in soil inorganic nutrient content, with higher concentrations of organic matter, total nitrogen, and available P2O5 and K+, which positively influenced blueberry growth. Mulching treatments demonstrated superior physiological characteristics, with two classification models identifying stomatal conductance (gs) as a key parameter influencing treatment classification (VIP scores > 1 rank: 3, variable score rank: 1). The photochemical reflectance index (PRI) emerged as a major parameter among VIs, showing potential for measuring water stress (VIP scores > 1 rank: 2, variable score rank: 1). In the SWIR PLS-DA model, wavelength peaks were mainly observed in the O-H overtone (1410 nm, 1450 nm, 1930 nm, 1940 nm, and 2100 nm). Overall, crushed pinecones were found to positively impact the initial growth of blueberries by enhancing water status (plant respiration).

Geographical origin discrimination of lemon myrtle (Backhousia citriodora) leaf powder using near-infrared hyperspectral imaging

Lemon myrtle (LM), Backhousia citriodora, is a popular flavouring agent and herbal tea from Australia. To ensure traceability and consumer trust in global food supply chain, rapid and non-destructive tools are crucial. In this study, hyperspectral images were acquired from 91 L M samples sourced from three different origins (Malaysia, Queensland, and New South Wales (Australia)), within 950–2500 nm range. Classification models were developed using linear partial least squares-discriminant analysis (PLS-DA) with two approaches, pixel-based (trained by all spectral data points) and sample-based (trained by average spectrum). All models achieved classification accuracies above 96%. The sample-based PLS-DA model, trained by mean-centring transformed data, demonstrated the highest discriminatory performance. Both approaches show potential for LM origin classification, but the sample-based model is more suitable for automated and rapid industry applications due to its shorter calculation time. However, additional spectral data acquisition is necessary to improve the model and fully explore its capabilities and limitations.

Multi-Sensor Classification Framework of Urban Vegetation for Improving Ecological Services Management

Recent climatic changes have profoundly impacted the urban microclimate, exposing city dwellers to harsh living conditions. One effective approach to mitigating these events involves incorporating more green infrastructure into the cityscape. The ecological services provided by urban vegetation play a crucial role in enhancing the sustainability and livability of cities. However, monitoring urban vegetation and accurately estimating its status pose challenges due to the heterogeneous nature of the urban environment. In response to this, the current study proposes utilizing a remote sensing-based classification framework to enhance data availability, thereby improving practices related to urban vegetation management. The aim of the current research is to explore the spatial pattern of vegetation and enhance the classification of tree species within diverse and complex urban environments. This study combines various remote sensing observations to enhance classification capabilities. High-resolution colored rectified aerial photographs, LiDAR-derived products, and hyperspectral data are merged and analyzed using advanced classifier methods, specifically partial least squares-discriminant analysis (PLS-DA) and object-based image analysis (OBIA). The OBIA method demonstrates an impressive overall accuracy of 95.30%, while the PLS-DA model excels with a remarkable overall accuracy of 100%. The findings validate the efficacy of incorporating OBIA, aerial photographs, LiDAR, and hyperspectral data in improving tree species classification and mapping within the context of PLS-DA. This classification framework holds significant potential for enhancing management practices and tools, thereby optimizing the ecological services provided by urban vegetation and fostering the development of sustainable cities.

Blood sphingolipid as a novel biomarker in patients with neuromyelitis optica spectrum disorder

BackgroundSphingolipids are signaling molecules and structural components of the axolemma and myelin sheath. Plasma sphingolipid levels may reflect disease status of neuromyelitis optica spectrum disorder (NMOSD). We aimed to examine plasma sphingolipids as disease severity biomarkers for NMOSD and compare their characteristics with those of serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP). MethodsWe measured plasma sphingolipids, sNfL, and sGFAP levels in NMOSD cases with anti-aquaporin-4-antibody. An unbiased approach, partial least square discriminant analysis (PLS-DA), was utilized to determine whether sphingolipid profiles differ according to the disease state of NMOSD (presence, moderate-to-severe disability [Expanded Disease Severity Scale, (EDSS) > 3.0], and relapses). ResultsWe investigated 81 patients and 10 controls. PLS-DA models utilizing sphingolipids successfully differentiated patients with EDSS > 3.0, but failed to identify the presence of disease and relapses. Ceramide-C14—a significant contributor to differentiating EDSS > 3.0—positively correlated with EDSS, while its levels were independent of age and the presence of relapses. This characteristic was unique from those of sNfL and sGFAP, which were affected by age and relapses as well as EDSS. ConclusionPlasma sphingolipids may be useful NMOSD biomarkers for disability with distinct characteristics compared to sNfL and sGFAP.

Geographic origin classification and simultaneous determination of methylxanthines in vietnamese tea using chemometrics based on the near infrared reflectance spectroscopy

This paper reported the results of classification of geographic origin and simultaneous analysis of three methylxanthines (caffeine, theobromine, theophylline) in Vietnamese tea samples by the infrared reflectance spectrophotometry coupled with chemometrics. The spectral range was 10,000-4,000cm-1 and each spectrum was measured at 2 cm-1 intervals. For the purpose of geographic origin classification, this study used FT-NIR spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA), and Principal Component Analysis-Discriminant Analysis (PCA-DA). The ability to determine the origin of tea samples in the prediction set of PLS-DA model is 100%. Using the same IR spectral database combined with the partial least squares (PLS), three methylxanthines in tea samples are also quickly quantified. The PLS model based on the spectra of 24 tea samples in which the contents of 3 analytes were determined by high performance liquid chromatography- HPLC) were applied for simultaneous determination of caffeine, theobromine and theophylline in samples. The determination of methylxanthines in 7 tea samples in test set gave the good accuracy of the PLS model. The correlation coefficients (R2) in the prediction set were of 0.9582, 0.8894 and 0.9303 for theobromine, theophylline, and caffeine, respectively. This work demonstrated that infrared reflectance spectrophotometry combined with chemometrics could be applied to rapidly classify the geographic origin and simultaneous determination of main contents in green tea.

Metabolic and elemental profiling as potential discriminating features among the black mahlab seeds (Monechma ciliatum) grown in three different regions.

Black mahlab (Monechma ciliatum) seed is a rich source of metabolites and minerals and is usually believed to have a similar composition between different areas of cultivation. Until now, no studies have assessed changes in black mahlab seeds (BMSs) to determine those constituents that help to discriminate them according to geographical origin. The present study attempted to compare the metabolomics and elemental profiles of BMSs of different geographical origins and identified the potential markers using ultrahigh-performance liquid chromatography quadrupole Orbitrap tandem mass spectrometry (UHPLC-Q-Orbitrap-MS2 ), and inductively coupled plasma mass spectrometry (ICP-MS) techniques and established the chemometric model to identify the potential markers and discriminate them according to cultivation sites. In this work, data from metabolites analysis by UHPLC-Q-Orbitrap-MS2 and multi-elemental data obtained from ICP-MS were combined with chemometrics for tracing the geographical origin of BMSs. Principal component analysis (PCA) was used to evaluate the overall grouping of samples. In contrast, partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed for authentication. PLS-DA and OPLS-DA models were fully validated (R2 Y and Q2 values > 0.5). Variable importance of various projections was applied to obtain valuable data about differential elements (seven markers were identified) and metabolites (23 markers were identified) with high discrimination potential. The outcomes presented in this study serve as an appropriate framework for developing novel discrimination approaches in food origin screening.

Honey produced by apini and meliponini in Brazil: multivariate analysis of physicochemical parameters, sugar and metabolite profiles

The objective of this study was to investigate honey samples from two bee tribes grouped in Meliponini (stingless bee) and Apini (Apis mellifera) honeys from Brazil, in order to compare their chemical composition. Thirty-four honey samples were collected and evaluated by proton NMR spectroscopy, HPLC and physicochemical parameters, using chemometric methods. Principal component analysis (PCA) was able to discriminate Apini and Meliponini goups. Additionally, the partial least squares discriminant analysis (PLS-DA) models were able to predict the Apini and Meliponini honey samples with sensitivity, specificity and accuracy values close to 100%. Frequency values between 4.00 − 3.00 ppm, corresponding to the sugar region, were important for the classification by the PLS-DA model using NMR data. For the PLS-DA model using physicochemical data, the main parameters for classification were water activity, reducing sugar, moisture, refractive index, total soluble solids, maltose and glucose. Since stingless honeys have no regulation, the data presented in this study can compose a database to assist in the establishment of regulatory criteria. Moreover, the chemometric models proposed can be used as a tool for the quality control of honey, especially for authentication purposes.

Classification of asbestos and their nonasbestiform analogues using FTIR and multivariate data analysis

This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition.

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Enhancing age-related postural sway classification using partial least squares-discriminant analysis and hybrid feature set

Feature sets in a machine learning algorithm can have an impact on the robustness, interpretability, and characterization of the data. To detect age-related changes, traditional linear methods for analyzing center of pressure (COP) signals offer limited insight into the complex nonlinear dynamics of postural control. To overcome this limitation, a novel approach that combines a partial least squares-discriminant analysis (PLS-DA) classifier with the nonlinear dynamics of COP time series was proposed. Three small feature sets were compared: time-domain features alone, entropy-based features alone, and a hybrid approach incorporating both types of features. The performance of the PLS-DA model was assessed in four different eyes and surface conditions by using the accuracy, sensitivity, selectivity, precision metrics, and ROC curves. The results indicated that the PLS-DA model utilizing the hybrid feature set achieved significantly higher accuracy than the time-domain and entropy-based feature sets. The best classification performance was observed when the eyes were open on a compliant surface, with an overall accuracy of 89% for training and 88% for cross-validation. For the old group, while the results indicated 93% sensitivity, 94% specificity, and 93% precision in the training, the results revealed 88% sensitivity, 93% specificity, and 91% precision in cross-validation. Notably, the hybrid feature set yielded an AUC value of 0.96, indicating a superior performance. This study emphasizes the robust classification capabilities of PLS-DA for age-related postural changes and highlights the effectiveness of utilizing a small hybrid feature set to improve classification accuracy and reliability.

Application of Near-Infrared Spectroscopy to Rapidly Classify the Chinese Quince Fruits from Different Habitats

The ecological habitats of Chinese quince (Chaenomeles speciosa Nakai) fruits affect their phenotype. Currently, limited or no rapid method exists for classifying Chinese quince fruit from different ecosystems. This study developed a partial least squares discriminant analysis (PLS-DA) classification model to effectively and nondestructively classify 663 Chinese quince fruit samples from six environments in 2020. PLS-DA models and other variable selection approaches were used in this study. The near-infrared spectroscopy (NIRs) absorption spectra of raw Chinese quince fruit samples from six habitats showed a similar shape. The spectra of each environment showed little variance. The raw fruit spectra varied significantly among habitat categories after the first derivative preprocessing phase. The uninformative variable elimination (UVE) variable selection approach had greater calibration and validation set specificity of 0.93 and 0.98. This study found the best classification specificity using the UVE variable selection approach compared to other methods including the PLS-DA model without variable selection. The UVE approach improved Yunnan habitat categorization specificity from 86% to 88% when integrated with PLS-DA. Additionally, the validation set for quinces originating from Anhui, Chongqing, Hubei, Shandong, and Zhejiang achieved an ideal classification score of 100%. The findings of the study indicated that PLS-DA can serve as an alternative approach for classifying the habitats of Chinese quince fruits. When used in conjunction with other methods, this technique can assist researchers, scientists, and industry professionals in identifying the main factors responsible for significant variations in the habitats, composition, and quality of Chinese quince fruits.

Surface-enhanced Raman Spectroscopy in urinalysis of hypertension patients with kidney disease

Arterial hypertension (AH) is a multifactorial and asymptomatic disease that affects vital organs such as the kidneys and heart. Considering its prevalence and the associated severe health repercussions, hypertension has become a disease of great relevance for public health across the globe. Conventionally, the classification of an individual as hypertensive or non-hypertensive is conducted through ambulatory blood pressure monitoring over a 24-h period. Although this method provides a reliable diagnosis, it has notable limitations, such as additional costs, intolerance experienced by some patients, and interferences derived from physical activities. Moreover, some patients with significant renal impairment may not present proteinuria. Accordingly, alternative methodologies are applied for the classification of individuals as hypertensive or non-hypertensive, such as the detection of metabolites in urine samples through liquid chromatography or mass spectrometry. However, the high cost of these techniques limits their applicability for clinical use. Consequently, an alternative methodology was developed for the detection of molecular patterns in urine collected from hypertension patients. This study generated a direct discrimination model for hypertensive and non-hypertensive individuals through the amplification of Raman signals in urine samples based on gold nanoparticles and supported by chemometric techniques such as partial least squares-discriminant analysis (PLS-DA). Specifically, 162 patient urine samples were used to create a PLS-DA model. These samples included 87 urine samples from patients diagnosed with hypertension and 75 samples from non-hypertensive volunteers. In the AH group, 35 patients were diagnosed with kidney damage and were further classified into a subgroup termed (RAH). The PLS-DA model with 4 latent variables (LV) was used to classify the hypertensive patients with external validation prediction (P) sensitivity of 86.4%, P specificity of 77.8%, and P accuracy of 82.5%. This study demonstrates the ability of surface-enhanced Raman spectroscopy to differentiate between hypertensive and non-hypertensive patients through urine samples, representing a significant advance in the detection and management of AH. Additionally, the same model was then used to discriminate only patients diagnosed with renal damage and controls with a P sensitivity of 100%, P specificity of 77.8%, and P accuracy of 82.5%.