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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]

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.

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]

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]

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.

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.

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]

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

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]

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.

Hepatic cirrhosis is a severe chronic disease of the liver accompanied by massive changes in the physiology of the cells constituting the hepatic tissue. Success or failing of a therapeutic effort is difficult to recognize because of its late manifestation in the tissue morphology. In this study, the complex course of hepatic cirrhosis and its regression is followed in a rodent carbon tetrachloride model. Raman spectroscopy, which senses molecular vibrations and reflects the molecular composition of a sample, was applied to gain label- and destruction-free insights into the process of cirrhosis and to conclude on the hepatic disease state on the cellular level. Hematoxylin and eosin staining and immunofluorescence labeling were used to obtain complementary information. False color images derived from maps of Raman spectra by spectral unmixing revealed individual nucleus positions giving structural information. Spectral data unraveled chemical changes associated with liver damage on the cellular level. Upon carbon tetrachloride treatment, a higher lipid content and the presence of catabolic products indicated cirrhosis in tissue samples. Furthermore, 77 % of the Raman spectra recorded from treated rats were classified as diseased, whereas 96 % of the Raman spectra recorded from untreated rats were classified as healthy. Importantly, samples from rats that experienced a recovery period revealed a chemical composition highly similar to the ones from healthy rats while morphologically clear signs of tissue damage were still obvious. Eighty-three percent of their Raman spectra were classified as healthy. The vibrational fingerprint of tissue provides characteristic information that might serve as prognostic biomarker. [ABSTRACT FROM AUTHOR]

Glycated hemoglobin (HbA1c) has been increasingly accepted as the gold standard for diabetes monitoring. In this study, Raman spectroscopy was tentatively employed for human hemoglobin (Hb) biochemical analysis aimed at developing a simple blood test for diabetes monitoring. Raman spectroscopy measurements were performed on hemoglobin samples of patients (n = 39) with confirmed diabetes and healthy volunteers (n = 37). The tentative assignments of the measured Raman bands were performed to compare the difference between these two groups. Meanwhile, principal component analysis (PCA) combined with linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification between normal controls and patients with diabetes. As a result, the spectral features of these two groups demonstrated two distinct clusters with a sensitivity and specificity of 92.3% and 73%, respectively. Then the effectiveness of the diagnostic algorithm based on PCA-LDA technique was confirmed by receiver operating characteristic (ROC) curve. The area under the ROC curve was 0.92, indicating a good diagnostic result. In summary, our preliminary results demonstrate that proposing Raman spectroscopy can provide a significant potential for the noninvasive detection of diabetes.

Glycated hemoglobin (HbA1c) has been increasingly accepted as the gold standard for diabetes monitoring. In this study, Raman spectroscopy was tentatively employed for human hemoglobin (Hb) biochemical analysis aimed at developing a simple blood test for diabetes monitoring. Raman spectroscopy measurements were performed on hemoglobin samples of patients (n = 39) with confirmed diabetes and healthy volunteers (n = 37). The tentative assignments of the measured Raman bands were performed to compare the difference between these two groups. Meanwhile, principal component analysis (PCA) combined with linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification between normal controls and patients with diabetes. As a result, the spectral features of these two groups demonstrated two distinct clusters with a sensitivity and specificity of 92.3% and 73%, respectively. Then the effectiveness of the diagnostic algorithm based on PCA-LDA technique was confirmed by receiver operating characteristic (ROC) curve. The area under the ROC curve was 0.92, indicating a good diagnostic result. In summary, our preliminary results demonstrate that proposing Raman spectroscopy can provide a significant potential for the noninvasive detection of diabetes. [ABSTRACT FROM AUTHOR]

Vibrational spectroscopy techniques can be applied to identify a susceptibility-to-adenocarcinoma biochemical signature. A sevenfold difference in incidence of prostate adenocarcinoma (CaP) remains apparent amongst populations of low- (e.g. India) compared with high-risk (e.g. UK) regions, with migrant studies implicating environmental and/or lifestyle/dietary causative factors. This study set out to determine the biospectroscopy-derived spectral differences between risk-associated cohorts to CaP. Benign prostate tissues were obtained using transurethral resection from high-risk ( n = 11, UK) and low-risk ( n = 14, India) cohorts. Samples were analysed using attenuated total reflection Fourier-transform infrared (FTIR) spectroscopy, FTIR microspectroscopy and Raman microspectroscopy. Spectra were subsequently processed within the biochemical cell region (1,800-500 cm) employing principal component analysis (PCA) and linear discriminant analysis (LDA) to determine whether wavenumber-absorbance/intensity relationships might reveal biochemical differences associated with region-specific susceptibility to CaP. PCA-LDA scores and corresponding cluster vector plots identified pivotal segregating biomarkers as 1,582 cm (Amide I/II trough); 1,551 cm (Amide II); 1,667 cm (Amide I); 1,080 cm (DNA/RNA); 1,541 cm (Amide II); 1,468 cm (protein); 1,232 cm (DNA); 1,003 cm (phenylalanine); 1,632 cm [right-hand side (RHS) Amide I] for glandular epithelium ( P < 0.0001) and 1,663 cm (Amide I); 1,624 cm (RHS Amide I); 1,126 cm (RNA); 1,761, 1,782, 1,497 cm (RHS Amide II); 1,003 cm (phenylalanine); and 1,624 cm (RHS Amide I) for adjacent stroma ( P < 0.0001). Primarily protein secondary structure variations were biomolecular markers responsible for cohort segregation with DNA alterations exclusively located in the glandular epithelial layers. These biochemical differences may lend vital insights into the aetiology of CaP. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

In this study we aimed to investigate the effect of heat treatment on the spectral pattern of honey using near infrared spectroscopy (NIRS). For the research, sunflower, bastard indigo, and acacia honeys were collected from entrusted beekeepers. The honeys were not subject to any treatment before. Samples were treated at 40 °C, 60 °C, 80 °C, and 100 °C for 60, 120, 180, and 240 min. This resulted in 17 levels, including the untreated control samples. The 5-hydroxymethylfurfural (HMF) content of the honeys was determined using the Winkler method. NIRS spectra were recorded using a handheld instrument. Data analysis was performed using ANOVA for the HMF content and multivariate analysis for the NIRS data. For the latter, PCA, PCA-LDA, and PLSR models were built (using the 1300–1600 nm spectral range) and the wavelengths presenting the greatest change induced by the perturbations of temperature and time intervals were collected systematically, based on the difference spectra and the weights of the models. The most contributing wavelengths were used to visualize the spectral pattern changes on the aquagrams in the specific water matrix coordinates. Our results showed that the heat treatment highly contributed to the formation of free or less bonded water, however, the changes in the spectral pattern highly depended on the crystallization phase and the honey type. [ABSTRACT FROM AUTHOR]

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the clinical potential of Raman spectroscopy (RS) in detecting oral squamous cell carcinoma (OSCC) in tumor and healthy tissues in surgical resection specimens during surgery. Raman experiments were performed on cryopreserved specimens from patients with OSCC. Univariate and multivariate analysis was performed based on the fingerprint region (700–1800 cm − 1 ) of the Raman spectra. One hundred thirty-one ex-vivo Raman experiments were performed on 131 surgical resection specimens obtained from 67 patients. The principal component analysis (PCA) and partial least square (PLS) methods with linear discriminant analysis (LDA) were applied on an independent validation dataset. Both models were able to differentiate between the tissue types, but PLS–LDA showed 100% accuracy, sensitivity, and specificity. In this study, Raman measurements of fresh resection tissue specimens demonstrated that OSCC had significantly higher nucleic acid, protein, and several amino acid contents than adjacent healthy tissues. The specific spectral information obtained in this study can be used to develop an in vivo Raman spectroscopic method for the tumor-free resection boundary during surgery. [ABSTRACT FROM AUTHOR]

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. [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. [ABSTRACT FROM AUTHOR]

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer. [ABSTRACT FROM AUTHOR]