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The genus Hordeum (barley) represents an essential group within the Poaceae family, comprising diverse species with significant ecological and economic importance. This study aims to improve the infrageneric classification of Hordeum by integrating multiple analytical approaches based on morphological data. A comprehensive dataset of key morphological traits was compiled from a wide range of Hordeum accessions, including representatives from all major taxonomic groups within the genus. Understanding and classifying the evolutionary traits of barley species, particularly in terms of environmental adaptation, pest resistance, and productivity improvement, is essential. DNA-based classification methods allow precise molecular-level analysis but are resource-intensive, especially when large-scale processing is required. This study addresses these limitations by employing an integrative approach combining hierarchical clustering, Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), and Random Forest (RF) to analyze the compiled morphological datasets. Morphological clustering via hierarchical analysis revealed clear taxonomic distinctions, achieving 86.0% accuracy at the subgenus level and 83.1% at the section level. PCA-LDA further refined classification by identifying key traits such as seed width, area, and 100-seed weight as primary contributors, achieving perfect accuracy for the Hordeum section and high accuracy for species like Hordeum vulgare and Hordeum spontaneum. RF analysis enhanced classification performance, achieving 100% accuracy at the section level and high accuracy for species with sufficient data. This approach offers a new framework for classifying diverse barley species and contributes significantly to data-driven decision-making in breeding and conservation efforts, supporting a deeper understanding of barley's adaptive evolution in response to environmental changes. [ABSTRACT FROM AUTHOR]

To fight counterfeits, and to protect the consumer, the interest in certifying the origin of agricultural goods has been growing in recent years. In this context and to increase the accuracy of zoning models, multiple analytical techniques must be combined via a multivariate approach. During the sampling campaign, leaves and fruits (olives or drupes) were collected from multiple orchards and farms. By means of HPLC-DAD, metabolite levels were evaluated and combined with the trace and ultra-trace metal/metalloid levels evaluated by ICP-MS (QqQ). The combined dataset was then used to develop a model for geographical traceability. Furthermore, the mineral content of the soil, evaluated by means of ICP-MS, was correlated with both the mineral content in the leaves and drupes and the metabolomic profiles to further investigate the connection between the orchard's location and characteristics of the final products. [ABSTRACT FROM AUTHOR]

The interaction of phytopathogenic organisms and plants generates physiological and biochemical changes in the latter. However, the effects on the plants are rarely visible in the infection first stages. Novel optical techniques can help to improve the early detection of phytopathogenic organisms in tomato without the plant sacrifice. In this work, infrared spectroscopy and chemometric methods were used to determinate the effects of Fusarium oxysporum in tomato plants cultivated in pots, analyzing fully expanded leaves. Fusarium oxysporum was molecular identified and its pathogenicity was tested in vitro. Three plants treatments were evaluated for 55 days post infection in pots in greenhouse under semi-controlled conditions: control, water stress, and fungal inoculated (1 × 108 conidia/mL). Phenotypical results were followed twice a week for eight weeks; the phenotypical characteristics were very similar in almost all sampling times except in height, especially in the first 27 days post infection, after this time the height was similar in the three treatments. The stalk and root-dried matter analysis do not show statistical differences; however, the infrared results, acquired from the adaxial surface of leaves, show differences in peaks associated with salicylic acid, jasmonic acid, abscisic acid, and proline in the first 27 days post infection. The principal component analysis–linear discriminant analysis were used to distinguish subtle biochemical changes between the three treatments, facilitating the early detection of the pathogen and its monitoring over time. [ABSTRACT FROM AUTHOR]

To fight counterfeits, and to protect the consumer, the interest in certifying the origin of agricultural goods has been growing in recent years. In this context and to increase the accuracy of zoning models, multiple analytical techniques must be combined via a multivariate approach. During the sampling campaign, leaves and fruits (olives or drupes) were collected from multiple orchards and farms. By means of HPLC-DAD, metabolite levels were evaluated and combined with the trace and ultra-trace metal/metalloid levels evaluated by ICP-MS (QqQ). The combined dataset was then used to develop a model for geographical traceability. Furthermore, the mineral content of the soil, evaluated by means of ICP-MS, was correlated with both the mineral content in the leaves and drupes and the metabolomic profiles to further investigate the connection between the orchard’s location and characteristics of the final products.

• The recycling potential of waste wood could increase when knowing its composition. • The best recycling option is defined dividing waste wood into three categories. • To this aim three different classification models were developed and compared. • Results suggest the best setting for getting good classification accuracy. • Near Infrared Spectroscopy can be used as a tool for waste wood real-time sorting. Recycling of post-consumer waste wood material is becoming an increasingly appealing alternative to disposal. However, its huge heterogeneity is calling for an assessment of the material characteristics in order to define the best recycling option and intended reuse. In fact, waste wood comes into a variety of uses/types of wood, along with several levels of contamination, and it can be divided into different categories based on its composition and quality grade. This study provides the measurement of more than a hundred waste wood samples and their characterisation using a hand-held NIR spectrophotometer. Three classification methods, i.e. K-nearest Neighbours (KNN), Principal Component Analysis – Linear Discriminant Analysis (PCA-LDA) and PCA-KNN, have been compared to develop models for the sorting of waste wood in quality categories according to the best-suited reuse. In addition, the classification performance has been investigated as a function of the number of the spectral measurements of the sample and as the average of the spectral measurements. The results showed that PCA-KNN performs better than the other classification methods, especially when the material is ground to 5 cm of particle size and the spectral measurements are averaged across replicates (classification accuracy: 90.9 %). NIR spectroscopy, coupled with chemometrics, turned out to be a promising tool for the real-time sorting of waste wood material, ensuring a more accurate and sustainable waste wood management. Obtaining real-time information about the quality and characteristics of waste wood material translates into a decision of the best recycling option, increasing its recycling potential. [ABSTRACT FROM AUTHOR]

Pathogen infection can damage agricultural products, thereby reducing their economic value. Fusarium spp. is a fungal pathogen that infects potatoes (Solanum tuberosum L.) and causes dry rot. In this study, we utilized visible–near-infrared (Vis–NIR) and shortwave–near-infrared (SW–NIR) spectroscopy for the early detection of Fusarium spp. infection in potato tubers. The spectrometer used in this study analyzed the Vis–NIR (400–1,000 nm) and SW–NIR (970–1,700 nm) regions. A total of 183 potato (G2 “Granola L.” variety) samples were used. Among these, 93 samples were artificially inoculated with Fusarium solani mycelia, while 90 samples were left uninfected and considered the control group. The potato samples were stored at two different temperatures (12 and 25°C). Vis–NIR and SW–NIR spectra were analyzed by a chemometric method, namely principal component analysis with linear discriminant analysis (PCA–LDA), to differentiate healthy and infected potatoes. The PCA–LDA model based on Vis–NIR spectra exhibited a calibration accuracy of 80.26% and a reliability of 65%. Meanwhile, the PCA–LDA model based on SW–NIR spectra exhibited a calibration accuracy of 100% and a reliability of 97.30%. Overall, both methods demonstrated their suitability for differentiating potato tubers with Fusarium spp. fungal infection and healthy ones; however, the results suggest that SW–NIR spectroscopy is more effective than Vis–NIR spectroscopy. [ABSTRACT FROM AUTHOR]

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares–discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis–linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia. [ABSTRACT FROM AUTHOR]

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID‐19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post‐vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme‐linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS‐CoV‐2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID‐19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR‐FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID‐19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA‐LDA model and 91% accuracy for ANN, respectively. Based on this proof‐of‐concept study, we believe this method could offer a simple, label‐free, cost‐effective tool for detecting seroconversion against SARS‐CoV‐2. This approach could be used as an alternative to ELISA. [ABSTRACT FROM AUTHOR]

For several years, Fourier transform infrared (FTIR) microspectroscopy has been proving to be very promising for use in cytological diagnostics because of its capability of providing rapid and label-free biochemical information about cell samples. The adoption of FTIR as a clinical tool has been slowed because of the poor compatibility with cells deposited on glass slides, commonly used in clinical practice, because of the absorption of IR radiation by glassy materials in the 1000–1800 cm−1 spectral range. However, the possibility of also obtaining diagnostic information from the IR absorption spectra in the 2700–3700 cm−1 range (including few peaks related to vibrational modes in cell lipids and proteins) has recently emerged. In this work, we investigate the use of the FTIR technique in the 2700–3700 cm−1 range for diagnostic purposes about human colon cells grown on glass coverslips. In fact, using the principal components analysis (PCA) technique, we are able to discriminate FTIR spectra of healthy cells from those of cancerous ones, mainly due to the larger relative lipid content in the former compared to the latter. In addition, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to build classification models for unknown FTIR spectra with optimal accuracy. These results support the promotion of the translation of the FTIR technique as a complementary diagnostic tool in cytological routine practice. [ABSTRACT FROM AUTHOR]

Organic and non-organic soybean flours, although visually indifferent, have a significant difference in price and nutrition content. Therefore, the accurate authentication detection of organic soybean flour is necessary. Visible-near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods is a non-destructive technique applied to detect authentic or adulterated organic soybean flour. The spectra of organic, adulterated organic, and nonorganic soybean flours were captured using a Vis-NIR spectrometer at 350-1000 nm. The spectra were analyzed using partial least squares (PLS), principal component analysis (PCA), and the combination of these two with discriminant analysis (DA). The results showed that PCA using PC1 and PC2 could differentiate organic and non-organic soybean flours, whereas PC1 and PC4 can detect pure and adulterated organic soybean flours. The PCA-linear DA models showed 98.5% accuracy (Acc) for predicting pure organic and adulterated soybean flours and 100% Acc for predicting organic and non-organic flours. Moreover, PLS regression models resulted in a high R² of >95% for predicting organic and non-organic flours and pure and adulterated soybean flours. In addition, the PLS-DA models can differentiate organic from non-organic soybean flour and distinguish pure and adulterated soybean flours with 100% Acc and reliability. [ABSTRACT FROM AUTHOR]

The rapid identification and recognition of COVID-19 have been challenging since its outbreak. Multiple methods were developed to realize fast monitoring early to prevent and control the pandemic. In addition, it is difficult and unrealistic to apply the actual virus to study and research because of the highly infectious and pathogenic SARS-CoV-2. In this study, the virus-like models were designed and produced to replace the original virus as bio-threats. Three-dimensional excitation-emission matrix fluorescence and Raman spectroscopy were employed for differentiation and recognition among the produced bio-threats and other viruses, proteins, and bacteria. Combined with PCA and LDA analysis, the identification of the models for SARS-CoV-2 was achieved, reaching a correction of 88.9% and 96.3% after cross-validation, respectively. This idea might provide a possible pattern for detecting and controlling SARS-CoV-2 from the perspective of combining optics and algorithms, which could be applied in the early-warning system against COVID-19 or other bio-threats in the future. [ABSTRACT FROM AUTHOR]

Label-free surface-enhanced Raman scattering (SERS) analysis shows tremendous potential for the early diagnosis and screening of colon cancer, owing to the advantage of being noninvasive and sensitive. As a clinical diagnostic tool, however, the reproducibility of analytical methods is a priority. Herein, we successfully fabricated Ag NPs/cellulose nanocrystals/graphene oxide (Ag NPs/CNC/GO) nanocomposite film as a uniform SERS active substrate for label-free SERS analysis of clinical serum. The Ag NPs/CNC/GO suspensions by self-assembling GO into CNC solution through in-situ reduction method. Furthermore, we spin-coated the prepared suspensions on the bacterial cellulose membrane (BCM) to form Ag NPs/CNC/GO nanocomposite film. The nanofilm showed excellent sensitivity (LOD = 30 nM) and uniformity (RSD = 14.2%) for Nile Blue A detection. With a proof-of-concept demonstration for the label-free analysis of serum, the nanofilm combined with the principal component analysis-linear discriminant analysis (PCA-LDA) model can be effectively employed for colon cancer screening. The results showed that our model had an overall prediction accuracy of 84.1% for colon cancer (n = 28) and the normal (n = 28), and the specificity and sensitivity were 89.3% and 71.4%, respectively. This study indicated that label-free serum SERS analysis based on Ag NPs/CNC/GO nanocomposite film combined with machine learning holds promise for the early diagnosis of colon cancer. [ABSTRACT FROM AUTHOR]

This work presents a novel strategy for authentication of fruit juices. The methodology is based on an electrochemical method combined with chemometrics. In addition, the case of orange juice adulteration with grapefruit juices was studied using this methodology. The electrochemical fingerprint of different juices showed the influence from different polyphenols according to the type of fruit. First, Principal Component Analysis (PCA) was able to differentiate clusters of different juices, being the higher group distance within apple, orange, and grape juices samples. On the other hand, partial least-squares – discriminant analysis (PLS-DA) and PCA-linear discriminant analysis (LDA) were used as classification methods, obtaining better results for orange and apple models. Furthermore, good results were obtained for the authentication of orange juices, compared to other juices, with an error rate of 0.04 for cross-validation. In the case of adulteration of orange juice was detected using PLS-DA at an adulteration level as low as 1%. Regression vectors for all models highlighted identifiable potential values that could be related to the main polyphenols in each type of fruit. This electrochemical method is rapid, low-cost, and compatible with on-site analysis compared to other laborious analysis described in the literature. • Electrochemical fingerprint of fruit juices was explored. • Authentication of juices was performed combining electrochemistry and chemometrics. • The methodology was successfully applied for orange juice authentication. • A level of 1% of grapefruit juice adulteration was detected in orange juice. [ABSTRACT FROM AUTHOR]

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells. [ABSTRACT FROM AUTHOR]

Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models. [ABSTRACT FROM AUTHOR]

[Display omitted] • First study to report differentiation of foundation creams based on NIR spectroscopy and chemometrics. • 93% of spectra identified correctly using NIR and PCA-LDA. • Darker foundations shades tend to be classified less accurately than lighter ones. • Different substrates did not affect differentiation Accuracy, Sensitivity, or Specificity. • A rapid, non-destructive method of identifying cosmetic trace evidence. Cosmetic smears are a form of trace evidence that can link the crime scene, suspects, and victims. Foundation and lipstick are the most common sources of cosmetics that can easily smear, with most current research focused on the evidential analysis of lipsticks. This research aims to create a database of cosmetic foundations on different materials and to access the robustness of using Near-infrared with chemometrics as a non-destructive technique to identify unknown samples collected from a crime scene. Small amounts of six shades of three brands of foundations were smeared on clothing materials, which were then analysed with a combination of Near-infrared with chemometric analysis. Principle component analysis (PCA) was used to reduce data dimensionality and explore potential patterns in sample separation and Linear Discriminant Analysis (LDA) was utilised to assign unknown samples to one of the established classes. The selected techniques proved to be promising for database construction and as a preliminary method of analysis, with 93% of the spectra being correctly classified. Notably, darker foundation shades were less likely to be correctly classified (90% classified correctly) compared to lighter ones (96.7% classified correctly). This could not be improved with Standard Normal Variate (SNV) data pre-treatment or selecting specific NIR regions. This finding is of particular importance; according to the Crime Survey for England and Wales (year ending March 2020) police recorded sexual offences demonstrated that those in Mixed and Black or Black British ethnic groups were significantly more likely to be a victim of sexual assault compared to White, Asian or Other ethnic groups. It is, therefore, crucial to add a wide range of foundation shades, particularly of darker tones, to the future database. [ABSTRACT FROM AUTHOR]

For several years, Fourier transform infrared (FTIR) microspectroscopy has been proving to be very promising for use in cytological diagnostics because of its capability of providing rapid and label-free biochemical information about cell samples. The adoption of FTIR as a clinical tool has been slowed because of the poor compatibility with cells deposited on glass slides, commonly used in clinical practice, because of the absorption of IR radiation by glassy materials in the 1000–1800 cm−1 spectral range. However, the possibility of also obtaining diagnostic information from the IR absorption spectra in the 2700–3700 cm−1 range (including few peaks related to vibrational modes in cell lipids and proteins) has recently emerged. In this work, we investigate the use of the FTIR technique in the 2700–3700 cm−1 range for diagnostic purposes about human colon cells grown on glass coverslips. In fact, using the principal components analysis (PCA) technique, we are able to discriminate FTIR spectra of healthy cells from those of cancerous ones, mainly due to the larger relative lipid content in the former compared to the latter. In addition, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to build classification models for unknown FTIR spectra with optimal accuracy. These results support the promotion of the translation of the FTIR technique as a complementary diagnostic tool in cytological routine practice.

The quality of probiotics has been associated with bacteria and yeast strains' contents and their stability against conditioning factors. Near-infrared spectroscopy (NIRS), as a non-destructive, fast, real-time, and cost-effective analytical technique, can provide some advantages over more traditional food quality control methods in quality evaluation. The aim of our study was to evaluate the applicability of NIRS to the characterization and viability prediction of three commercial probiotic food supplement powders containing lactic acid bacteria (LAB) subjected to concentration and temperature conditioning factors. For each probiotic, 3 different concentrations were considered, and besides normal preparation (25 °C, control), samples were subjected to heat treatment at 60 or 90 °C and left to cool down until reaching room temperature prior to further analysis. Overall, after applying chemometrics to the NIR spectra, the obtained principal component analysis-based linear discriminant analysis (PCA-LDA) classification models showed a high accuracy in both recognition and prediction. The temperature has an important impact on the discrimination of samples. According to the concentration, the best models were identified for the 90 °C temperature treatment, reaching 100% average correct classification for recognition and over 90% for prediction. However, the prediction accuracy decreased substantially at lower temperatures. For the 25 °C temperature treatment, the prediction accuracy decreased to nearly 60% for 2 of the 3 probiotics. Moreover, according to the temperature level, both the recognition and prediction accuracies were close to 100%. Additionally, the partial least square regression (PLSR) model achieved respectable values for the prediction of the colony-forming units (log CFU/g) of the probiotic samples, with a determination coefficient for prediction (R2Pr) of 0.82 and root mean square error for prediction (RMSEP) of 0.64. The results of our study show that NIRS is a fast, reliable, and promising alternative to the conventional microbiology technique for the characterization and prediction of the viability of probiotic supplement drink preparations. [ABSTRACT FROM AUTHOR]

The emergence of paraffin-coated rice in China, aimed at enhancing its market appeal and achieving a translucent appearance, has given rise to a significant global food safety concern. This situation poses substantial health risks to consumers. Hyperspectral analysis, recognized as a powerful and nondestructive technique for assessing food quality and safety, offers a potential solution. This study conducted a comprehensive investigation using Visible-Near Infrared (VIS-NIR) hyperspectral imaging systems operating within the 400-1000 nm range to identify paraffin-contaminated rice. Various rice varieties from diverse regions were obtained and intentionally tainted with varying levels of paraffin. Imaged samples were further preprocessed for spectral data extraction from individual rice seeds’ regions of interest (ROI). The dataset encompassed 3000 spectral records obtained from both non-contaminated and contaminated samples. The obtained spectral data were employed to develop partial least squares discriminant analysis (PLS-DA) and principal component linear discriminant analysis. The primary goal was to discriminate between contaminated and non-contaminated rice samples effectively. Notably, the results indicated that PLS-DA consistently achieved an accuracy exceeding 94% across various preprocessing techniques. Overall, this study showcased the potential of combining hyperspectral imaging with chemometrics to detect paraffin-contaminated rice seeds, providing a valuable contribution to food safety assessment in the industry.

Choosing the cacao maturity stage is essential for producing high-quality cacao beans. Identifying indicators of the maturity level of cacao is a complex task because these fruits do not exhibit the characteristics of other fruits during the ripening period. Generally, cacao maturity is determined manually based on the estimated daily harvest date using sensory observation, which is marked by changes in the color of the cacao skin. This is certainly inaccurate because visual assessment is only performed subjectively. This is inaccurate because visual assessment is only performed subjectively, which is not in line with the demands of Industrial Revolution 4.0, which is a fast and accurate technology for sorting cacao. In this study, cacao maturity was identified using visible (350-1000 nm) and shortwave near-infrared spectra (SW-NIR) spectroscopy (1000-1600 nm). Chemometric analysis using principal component analysis-linear discriminant analysis (PCA-LDA) was used to classify cacao maturity. The results showed that SW-NIR spectroscopy yielded better performances with calibration and prediction accuracy of 92,50% and 85% using Savitzky–s 1st derivative (SGD1) spectra compared to Vis-NIR spectroscopy had calibration and prediction accuracies of 90% and 86% using raw spectra for PCA-LDA model.

The substrate employed in surface-enhanced Raman spectroscopy (SERS) constitutes an essential element in the cancer detection methodology. In this research, we introduce a three-dimensional (3D) structured SERS substrate that integrates a porous membrane with silver nanoparticles to enhance SERS spectral signals through the utilization of the aggregation effect of silver nanoparticles. This enhancement is crucial because accurate detection results strongly depend on the intensity of specific peaks in Raman spectroscopy. A highly sensitive SERS substrate can significantly improve the accuracy of detection results. We collected 66 plasma samples from individuals with kidney cancer and control individuals, including both bladder cancer patients and healthy individuals. Then, we utilized substrates with and without porous membranes to acquire the SERS spectra of the samples, enabling us to evaluate the enhancement effect of our SERS substrate. The spectral analysis demonstrated enhanced peak intensities in the experimental group (with porous substrate) compared to the control group (without porous substrate). The uniformity and reproducibility of the SERS substrate are also significantly enhanced, which is very helpful for improving the accuracy of detection results. Additionally, the Principal Component Analysis-Linear Discriminant Analysis algorithm (PCA-LDA) was employed to classify the SERS spectra of both groups. In the experimental group, the classification accuracy was 98.5 % for kidney cancer, and 83.3 % for kidney and bladder cancer. Compared to the control group, it improved by 3 % and 12.6 % respectively. This indicates that our 3D structured SERS substrate combined with multivariate statistical algorithms PCA-LDA can not only improve the accuracy of SERS detection technology in single cancer detection, but also has great potential in multiple cancer detection. This 3D structured SERS substrate is expected to become a new auxiliary means for cancer detection. This study presents a porous membrane-Ag NPs substrate that enhances SERS spectral signals by utilizing the aggregation effect of silver nanoparticles. We assessed the uniformity of hotspot imaging on the compound substrate within a 200 μm × 200 μm area at a wavelength of 1510 cm−1 and obtained excellent results. Additionally, we conducted a comparative analysis of 12 spectra obtained from different positions within the same area, indicating consistent and reliable repeatability. Overall, using PCA-LDA detection models, we can accurately distinguish the collected SERS spectra of individuals with and without kidney cancer, achieving an impressive accuracy rate of 98.5 %. Moreover, it can effectively differentiate the SERS spectra of individuals with kidney cancer, bladder cancer, and those in good health, achieving an accuracy of 81.8 %. [Display omitted] • The utilization of combined porous membranes with Ag NPs enables the acquisition of superior SERS spectra, surpassing those obtained with regular Ag NPs. • The detection process demonstrates excellent reproducibility and uniformity when using a porous membrane loaded with Ag NPs. • The accuracy of detection kidney cancer and differentiating healthy individuals using a porous membrane loaded with Ag NPs is significantly higher at 98.5 % compared to the use of ordinary Ag NPs substrate. [ABSTRACT FROM AUTHOR]

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease which can lead to a prolonged physical disability. HS diagnosis is exclusively clinical with the absence of biomarkers. Our study aims at assessing the HS‐diagnostic potential of infrared spectroscopy from saliva, as a biofluid reflecting the body's pathophysiological state. Infrared spectra from 127 patients (57 HS and 70 non‐HS) were processed by multivariate methods: principal component analysis coupled with Kruskal–Wallis or Mann–Whitney tests to identify discriminant spectral wavenumbers and linear discriminant analysis to evaluate the performances of HS‐diagnostic approach. Infrared features, mainly in the 1300 cm−1‐1600 cm−1 region, were identified as discriminant for HS and prediction models revealed diagnostic performances of about 80%. Tobacco and obesity, two main HS risk factors, do not seem to alter the infrared diagnosis. This pilot study shows the potential of salivary "liquid biopsy" associated to vibrational spectroscopy to develop a personalized medical approach for HS patients' management. [ABSTRACT FROM AUTHOR]

Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models.

Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metabolites, confirming that bands of uric acid, hypoxanthine, ergothioneine, and glutathione were indeed used by the PCA-LDA algorithm to classify the spectra. [ABSTRACT FROM AUTHOR]

Introduction: Non-destructive analysis of biological evidence has been paramount importance in the forensic investigation since it is an effective tool in establishing a standard that could be employed to differentiate ensuing destructive tests of bio-fluids upon sample division between the plaintiff and defendant. Species identification of bloodstain found at the crime scene is very crucial in routine forensic work as this can assist the initial investigation by incorporating or excluding stain that is not human and to identify its origin if animal blood is involved. Methods: In this research, identification and discrimination of various blood species collected from seven domestic animals namely chicken, cow, deer, duck, fish, goat, and pig were investigated using non-destructive analytical techniques; ATR-FTIR and visible spectroscopy coupled with principal component analysis and linear discriminant analysis (PCA-LDA) for classification purposes. Results: ATR-FTIR FTIR spectroscopic study demonstrated a higher rate of successful classification (≥ 90%) as compared to visible spectroscopic technique. Conclusion: ATR-FTIR spectroscopy has been an ideal, robust, and suitable tool for determining the blood species of domestic animals. The predictive model from PCA-LDA analysis can be utilised to produce higher classification rate for species determination from blood traces. [ABSTRACT FROM AUTHOR]

The 4000–400 cm−1 spectral information obtained by FTIR-ATR for bee pollen samples of different floral origin from various Romanian counties were used to formulate a method for rapid classification using principal component analysis (PCA) and linear discriminant analysis (LDA) as multivariate statistics tools. Entire IR range spectra were used as input data in both techniques. PCA reduced the problem dimensionality and put in evidence the presence of outliers. LDA, applied for samples characterized by the first 11 principal components (PCA-LDA), clearly separated 10 groups according to botanical and geographical origin, thus representing a base for developing an identification tool of the new pollen samples origin. Furthermore, spectral information and previously determined properties (polyphenols, flavonoids, sugars, proteins) were tested for identifying polynomial correlations using partial least squares (PLS) technique. The good correlation coefficients, R2 > 0.97, and low mean errors in both training and testing stages proved that a FTIR-ATR spectrum provides key information for rapid screening of bee pollen in terms of the parameters significant for its nutritional labelling. [ABSTRACT FROM AUTHOR]

The quality of probiotics has been associated with bacteria and yeast strains’ contents and their stability against conditioning factors. Near-infrared spectroscopy (NIRS), as a non-destructive, fast, real-time, and cost-effective analytical technique, can provide some advantages over more traditional food quality control methods in quality evaluation. The aim of our study was to evaluate the applicability of NIRS to the characterization and viability prediction of three commercial probiotic food supplement powders containing lactic acid bacteria (LAB) subjected to concentration and temperature conditioning factors. For each probiotic, 3 different concentrations were considered, and besides normal preparation (25 °C, control), samples were subjected to heat treatment at 60 or 90 °C and left to cool down until reaching room temperature prior to further analysis. Overall, after applying chemometrics to the NIR spectra, the obtained principal component analysis-based linear discriminant analysis (PCA-LDA) classification models showed a high accuracy in both recognition and prediction. The temperature has an important impact on the discrimination of samples. According to the concentration, the best models were identified for the 90 °C temperature treatment, reaching 100% average correct classification for recognition and over 90% for prediction. However, the prediction accuracy decreased substantially at lower temperatures. For the 25 °C temperature treatment, the prediction accuracy decreased to nearly 60% for 2 of the 3 probiotics. Moreover, according to the temperature level, both the recognition and prediction accuracies were close to 100%. Additionally, the partial least square regression (PLSR) model achieved respectable values for the prediction of the colony-forming units (log CFU/g) of the probiotic samples, with a determination coefficient for prediction (R2Pr) of 0.82 and root mean square error for prediction (RMSEP) of 0.64. The results of our study show that NIRS is a fast, reliable, and promising alternative to the conventional microbiology technique for the characterization and prediction of the viability of probiotic supplement drink preparations.

Na Chen,1 Ming Rong,1 Xiaoguang Shao,2 Heng Zhang,1 Shupeng Liu,1,3 Baijun Dong,2 Wei Xue,2 Tingyun Wang,1 Taihao Li,3 Jiahua Pan2 1Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, 2Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 3Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, People’s Republic of China Abstract: The surface-enhanced Raman spectroscopy (SERS) of blood serum was investigated to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) in males with a prostate-specific antigen level of 4–10 ng/mL, so as to reduce unnecessary biopsies. A total of 240 SERS spectra from blood serum were acquired from 40 PCa subjects and 40 BPH subjects who had all received prostate biopsies and were given a pathological diagnosis. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms, were used to analyze the spectra data of serum from patients in control (CTR), PCa and BPH groups; results offered a sensitivity of 97.5%, a specificity of 100.0%, a precision of 100.0% and an accuracy of 99.2% for CTR; a sensitivity of 90.0%, a specificity of 97.5%, a precision of 94.7% and an accuracy of 98.3% for BPH; a sensitivity of 95.0%, a specificity of 93.8%, a precision of 88.4% and an accuracy of 94.2% for PCa. Similarly, this technique can significantly differentiate low- and high-risk PCa with an accuracy of 92.3%, a specificity of 95% and a sensitivity of 89.5%. The results suggest that analyzing blood serum using SERS combined with PCA–LDA diagnostic algorithms is a promising clinical tool for PCa diagnosis and assessment. Keywords: Ag nanoparticles, linear discriminant analysis, gray zone, principle component analysis, benign prostatic hyperplasia, spectral classification

Despite the rapid development of medical science, the diagnosis of lung cancer is still quite challenging. Due to the ultrahigh detection sensitivity of surface-enhanced Raman spectroscopy (SERS), SERS has a broad application prospect in biomedicine, especially in the field of tumor blood detection. Although Raman spectroscopy can diagnose lung cancer through tissue slices, its weak cross sections are problematic. In this study, silver nanoparticles (AgNPs) were added to the surface of lung tissue slices to enhance the Raman scattering signals of biomolecules. The electromagnetic field distribution of AgNPs prepared was simulated using the COMSOL software. SERS obtained from the slices reflected the difference in biochemical molecules between normal (n = 23) and cancerous (n = 23) lung tissues. Principal component-linear discriminate analysis (PCA-LDA) was utilized to classify lung cancer and healthy lung tissues. The receiver operating characteristic curve gave the sensitivity (95.7%) and specificity (95.7%) of the PCA-LDA method. This study sheds new light on the general applicability of SERS analysis of tissue slices in clinical trials. [ABSTRACT FROM AUTHOR]

In this preliminary study, we employed surface-enhanced Raman scattering (SERS) of saliva and serum samples for diagnosing Sjogren's syndrome (SjS), a systemic autoimmune disease characterized by dryness of the mouth and eyes. The saliva and serum samples from n = 29 patients with SjS and n = 21 controls were deproteinized with methanol and then the SERS spectra were acquired using silver nanoparticles synthesized by reduction with hydroxylamine hydrochloride. In the case of both saliva and serum, the SERS spectra were dominated by similar bands attributed to purine metabolites such as uric acid, xanthine, and hypoxanthine. Principal component analysis-linear discriminant analysis (PCA-LDA) models built from SERS spectra of saliva and serum yielded an overall classification accuracy of 94% and 98%, respectively. These results suggest that the SERS analysis of saliva and serum is able to capture the complex biochemical perturbations that accompany the onset of SjS, a strategy which could be translated in the future into a novel point-of-care diagnosis method. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is non-destructive method that allows monitoring of biological tissues with minimal intervention. FT-Raman (λ ex 1064 nm) and NIR-Vis-Raman (λ ex 785 nm) spectroscopic measurements were used in ex vivo analysis of normal, non-cancerous abnormal and cancerous lung tissues. Spectroscopic discrimination of the lung tissue samples was made by the use of the ratio of characteristic bands and multivariate statistical methods (PCA, LDA). The combination of Raman spectroscopy and multivariate statistics may have a diagnostic potential for recognizing of cancer lesions in lung. Unlabelled Image • Raman spectroscopy allows monitoring of biological tissues with minimal intervention. • Raman measurements at λ ex 1064 nm and 785 nm were used in ex vivo analysis of lung tissues. • Normal, non-cancerous abnormal and cancerous samples were discriminated by statistical evaluation of the spectra. • Raman spectroscopy may have a diagnostic potential for recognizing of cancer lesions in lung. [ABSTRACT FROM AUTHOR]

Highlights • Measurements of the FTIR spectra of normal, FTA and WI-FTC thyroid tissues in the range 900–3500 cm−1. • Presenting of changes in the structure of proteins in the three kind of thyroid tissues. • FTIR combined with PCA-LDA analysis as a new thyroid diseases diagnostic method. Abstract Thyroid follicular nodules are quite common in the population, however only a small proportion is malignant. Thyroid cancer differs from adenoma by features of cellular atypia, angioinvasiveness and possibility of metastasis via blood vessels mainly in the lungs and bones. Pathomorphological examination of the postoperative material plays a significant role in the diagnosis of cystic thyroid lesions. De facto, there is no possibility to determine with certainty whether the lesion is benign or malignant before surgery, therefore new methods are being sought to meet clinical needs. The study aimed to investigate if Fourier-transform infrared spectroscopy (FTIR) spectroscopy and Raman spectroscopy combined with multidimensional analysis can be a useful tool in distinguishing between thyroid adenomas and carcinomas. The obtained results indicate quantitative and qualitative alterations within proteins and fats derived from patients' tissues samples. Raman spectroscopy additionally shows significant changes in the amount of tissue collagen due to the pathogenic process. In the spectra of the second FTIR derivative, shifts of vibrations corresponding to the β-sheet and α-helix structure are observed towards the lower rates of wave numbers in the case of neoplastic tissues. Using the leave-one-out cross-validation, sensitivity and specificity calculated with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) clearly shows the possibility to distinguish between pathologically changed and normal thyroid tissue as well as differentiate follicular thyroid adenoma (FTA) from widely invasive follicular thyroid carcinoma (WI-FTC) tissues. [ABSTRACT FROM AUTHOR]

Todays, it is essential to evaluate and check the quality of herbal medicines in to protect the public health. As medicinal plants, the extracts of labiate herbs are used directly or indirectly to treat a variety of diseases. Increase in their consumption has led to the fraud in herbal medicines. Hence, modern accurate diagnostic methods must be introduced to differentiate and authenticate these samples. Electrochemical fingerprints have not been evaluated for their capacity to distinguish and classify various genera within a family. Since it is essential to classify, identify, and distinguish between these closely related plants in order to guarantee the quality of the raw materials, the authenticity and quality of 48 dried and fresh Lamiaceae samples, which include Mint, Thyme, Oregano, Satureja, Basil, and Lavender with various geographic origins, were examined. The present study focused on (a) classification and authentication Labiate herbs extracts and (b) identification of active compounds in samples by Gas chromatography and HPLC methods. This was accomplished using principal component analysis (PCA) and PCA-linear discriminate analysis (PCA-LDA). The results of the clustering revealed that PCA-LDA categorized mint species more accurately than PCA. In addition to certain flavonoids including ferulic acid, apigenin, luteolin, and quercetin, HPLC and GC analysis of the ethanolic extract revealed the presence of phenolic acids such as rosmarenic acid, methyl rosmarenate, caffeic acid, cinnamic acid, and chlorogenic acid. Comparing results of PCA-LDA with chromatographic analysis show that the authentication and detection of fraud samples were correctly performed using chemometyrics technique based on CV fingerprints. Even, there was no need to completely identify components of the mint samples. [Display omitted] • Identification and discrimination of closely-related Lamiaceae plants are a crucial task to quality control of raw materials. • This work extended the electrochemical classification as rapid and accurate to family level for the first time. • Electrochemical profiles of Lamiaceae family shows a different finger prints for various genera of species. • The species within family can be identified by PCA-LDA pattern recognition modes. • Mint species by rosmarinic acid and caffeic acid; basil and lavender: rosmarinic acid; Oregano and lavender by quercetin and thyme by thymol contents classified. [ABSTRACT FROM AUTHOR]

The postharvest processing factors including cherry processing methods highly influence the final quality of coffee beverages, especially in the composition of several coffee metabolites such as glucose, fructose, the amino acid (glutamic acid), and chlorogenic acids (CGA) as well as trigonelline contents. In this research, UV spectroscopy combined with chemometrics was used to classify a ground roasted Lampung robusta specialty coffee according to differences in the cherry processing methods. A total of 360 samples of Lampung robusta specialty coffee with 1 g of weight for each sample from three different cherry processing methods were prepared as samples: 100 samples of pure dry coffee (DRY), 100 samples of pure semi-dry coffee (SMD), 100 samples of pure wet coffee (WET) and 60 samples of adulterated coffee (ADT) (SMD coffee was adulterated with DRY and WET coffee). All samples were extracted using a standard protocol as explained by previous works. A low-cost benchtop UV-visible spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, Waltham, MA, USA) was utilized to obtain UV spectral data in the interval of 190–400 nm using the fast scanning mode. Using the first three principal components (PCs) with a total of 93% of explained variance, there was a clear separation between samples. The samples were clustered into four possible groups according to differences in cherry processing methods: dry, semi-dry, wet, and adulterated. Four supervised classification methods, partial least squares–discriminant analysis (PLS-DA), principal component analysis–linear discriminant analysis (PCA-LDA), linear discriminant analysis (LDA) and support vector machine classification (SVMC) were selected to classify the Lampung robusta specialty coffee according to differences in the cherry processing methods. PCA-LDA is the best classification method with 91.7% classification accuracy in prediction. PLS-DA, LDA and SVMC give an accuracy of 56.7%, 80.0% and 85.0%, respectively. The present research suggested that UV spectroscopy combining with chemometrics will be highly useful in Lampung robusta specialty coffee authentication.

Breast cancer is one of the major cancers of women in the world. Despite significant progress in its treatment, an early diagnosis can effectively reduce its incidence rate and mortality. To improve the reliability of Raman-based tumor detection and analysis methods, we conducted an ex vivo study to unveil the compositional features of healthy control (HC), solid papillary carcinoma (SPC), mucinous carcinoma (MC), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) tissue samples. Following the identification of biological variations occurring as a result of cancer invasion, principal component analysis followed by linear discriminate analysis (PCA-LDA) algorithm were adopted to distinguish spectral variations among different breast tissue groups. The achieved results confirmed that after training, the constructed classification model combined with the leave-one-out cross-validation (LOOCV) method was able to distinguish the different breast tissue types with 100% overall accuracy. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

Alzheimer's disease (AD) is a disease of advanced civilization and a common form of dementia in people over 65 years of age. We used Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA) to determine changes in the quantity and quality of the cerebrospinal fluid from AD patients at three different stages of the disease (ADI, ADII, and ADIII), as well as from patients with mild cognitive impairment (MCI). Moreover, based on the FTIR spectra, we calculated the ratio of α-helix and β-sheet secondary protein structures as well as the lipid-protein balance as potential AD markers. The FTIR spectra of cerebrospinal fluid obtained from MCI, ADI, ADII, and ADIII patients showed that peaks corresponding to protein and deoxyribonucleic acid (DNA), and phospholipid and lipid vibrations were shifted in comparison with those of control subjects. Furthermore, the levels of these chemical compounds were lower in the patients than in the control subjects. The β-sheet secondary protein structure levels were increased in the MCI and AD patients compared with the control subjects. In addition, significant changes in the lipid-protein balance were observed. Interestingly, as the disease progressed, the lipid-protein balance became further disrupted, that is, the lipid amount decreased with disease progression. PCA analysis of lipid-protein FTIR regions revealed that the spectra could be used to distinguish between controls and patients with MCI, ADI, ADII, and ADIII. [ABSTRACT FROM AUTHOR]

Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. [ABSTRACT FROM AUTHOR]

Macadamia nut industry is increasingly gaining more space in the food market and the success of the industry and the quality are largely due to the selection of cultivars through macadamia nut breeding programs. Thus, the objective of this study was to investigate the feasibility NIRS coupled to chemometric classification methods, to build a rapid and non-invasive analytical procedure to classify different macadamia cultivars based on intact nuts. Intact nuts of five different macadamia cultivars (HAES 246, IAC 4-20, IAC 2-23, IAC 5-10, and IAC 8-17) were harvested in 2017. Two NIR reflectance spectra were collected per nut, and the mean spectra were used to chemometrics analysis. Principal component analysis-linear discriminant analysis (PCA-LDA) and genetic algorithm-linear discriminant analysis (GA-LDA) were used to develop the classifications models. The GA-LDA approach resulted in accuracy higher than 94.44%, with spectra preprocessed with Savitzky-Golay smoothing. Thus, this approach can be implemented in the macadamia industry, allowing the selection of cultivars based on intact nuts. However, it is recommended that more experimentation to include more data variability in order to increase the classification accuracy to 100%. [ABSTRACT FROM AUTHOR]

Abstract: Currently, the qualification and control of medium formulations are performed based on simple methods (eg, pH and osmolality measurement of medium solutions), expensive and time‐consuming cell culture tests, and the quantification of certain critical compounds by liquid chromatography. In addition to traditional medium qualification tools, relatively new spectroscopic techniques, such as fluorescence spectroscopy, nuclear magnetic resonance, Raman and near‐infrared spectroscopies, and combinations of these techniques are increasingly being applied to medium powder investigation. A chemically defined medium powder for Chinese hamster ovary cell cultivation was investigated in this study to determine its response to heat treatments at different temperatures (30°C, 50°C, and 70°C). Because the low availability and high costs of medium powders limit the sample sizes for such experiments, 5 groups of simulated data sets were generated based on the experimental spectra to compare the efficiencies of 3 classification methods: linear discriminant analysis (LDA) based on principal component analysis (PCA), partial least squares discriminant analysis (PLS‐DA), and soft independent modeling of class analogy (SIMCA). In case of these data sets, PCA‐LDA showed better results for the classification of experimental spectra than PLS‐DA and SIMCA. Moreover, the PLS‐DA and SIMCA models yielded different results for different training set groups, while the PCA‐LDA model yielded similar results for all training sets. [ABSTRACT FROM AUTHOR]

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology. [ABSTRACT FROM AUTHOR]

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site. [ABSTRACT FROM AUTHOR]

Recently, food authenticity has raised worldwide attention in food manufacturing and a growing concern about food qualification, based on a clear regional identity, is noticed. Therefore, the development of suitable methodologies allowing the characterization of different products, based on their geographical origin, is of great importance. In this study, the potential of gas chromatographic fatty acid fingerprints in combination with multivariate data analysis was examined to classify walnuts from different regions in Iran according to their geographical origins. Walnut samples were collected during the harvesting period 2013–2014 from six regions in Iran. Chromatographic fingerprints of the walnut oil were employed to discriminate the walnut origin. Principal component analysis-Linear discriminant analysis (PCA-LDA) results showed that the six regions of geographical origin can be identified based on the fatty acid fingerprints. Almost all samples were correctly classified by the PCA-LDA model using cross validation (99.2%). The average percent correct classification for the prediction set was 98.3%, indicating the satisfactory performance of the model. A high percentage of correct classifications for the training data demonstrates the strong relationship between the fatty acid profile and the origin, while a high percentage for the prediction set shows the ability to indicate the origin of an unknown sample based on its fatty acid chromatographic data. [ABSTRACT FROM AUTHOR]

In this study, adulteration of almond powder samples with peanut was analyzed using multi-elemental fingerprinting based on inductively coupled plasma optical emission measurements (ICP-OES) combined with chemometric methods. The ability of multivariate data analysis approaches, such as principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA), to achieve differentiation of samples and as partial least squares (PLS) and least squares support vector machine (LS-SVM), to quantify the adulteration based on the elemental contents has been investigated. Ten variables i.e. the contents of B, Na, Mg, K, Ca, Fe, Cu, Cu, Zn and Sr at μg g −1 level, determined by ICP-OES were used. Different almond and peanut samples were then mixed at various ratios to obtain mixtures ranging from 95/5 to 5/95 w/w and PCA-LDA was applied to classify the almonds, peanuts and adulterated samples. This method was able to differentiate peanut and almond samples from the adulterated samples. PLS and LS-SVM models were developed to quantify the adulteration ratios of almond using a training set and the constructed models were evaluated using a validation set. The root mean squared error of prediction (RMSEP) and the coefficient of determination (R 2 ) of the validation set for PLS and LS-SVM were 3.81, 0.986 and 1.66, 0.997, respectively, which demonstrates the superiority of the LS-SVM model. The results show that the combination of multi-elemental fingerprinting with multivariate data analysis methods can be applied as an effective and feasible method for testing almond adulteration. [ABSTRACT FROM AUTHOR]