Automatic Classification of Antimalarial Herbal Drugs Exposed to Ultraviolet Radiation from Unexposed Ones Using Laser-Induced Autofluorescence with Chemometric Techniques.

Exposure of antimalarial herbal drugs (AMHDs) to ultraviolet radiation (UVR) affects the potency and integrity of the AMHDs. Instant classification of the AMHDs exposed to UVR (UVR-AMHDs) from unexposed ones (Non-UVR-AMHDs) would be beneficial for public health safety, especially in warm regions. For the first time, this work combined laser-induced autofluorescence (LIAF) with chemometric techniques to classify UVR-AMHDs from Non-UVR-AMHDs. LIAF spectra data were recorded from 200 ml of each of the UVR-AMHDs and Non-UVR-AMHDs. To extract useful data from the spectra fingerprint, principal components (PCs) analysis was used. The performance of five chemometric algorithms: random forest (RF), neural network (NN), support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbour (KNN), were compared after optimization by validation. The chemometric algorithms showed that KNN, SVM, NN, and RF were superior with a classification accuracy of 100% for UVR-AMHDs while LDA had a classification accuracy of 98.8% after standardization of the spectra data and was used as an input variable for the model. Meanwhile, a classification accuracy of 100% was obtained for KNN, LDA, SVM, and NN when the raw spectra data was used as input except for RF for which a classification accuracy of 99.9% was obtained. Classification accuracy above 99.74 ± 0.26% at 3 PCs in both the training and testing sets were obtained from the chemometric models. The results showed that the LIAF, combined with the chemometric techniques, can be used to classify UVR-AMHDs from Non-UVR-AMHDs for consumer confidence in malaria-prone regions. The technique offers a non-destructive, rapid, and viable tool for identifying UVR-AMHDs in resource-poor countries.

Classification of Organic and Conventional Cocoa Beans Using Laser-Induced Fluorescence Spectroscopy Combined with Chemometric Techniques.

The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.

The electronic tongue: an advanced taste-sensing multichannel sensory tool with global selectivity for application in the pharmaceutical and food industry.

Taste is a crucial organoleptic characteristic that determines whether a substance will be accepted for delivery through the mouth. However, the vast majority of medications have an unpleasant taste. Drugs with a bitter taste are often depicted using a variety of flavouring compounds to increase patient acceptance and compliance. Human panellists are the principal means of assessing the flavour of medication ingredients and formulations. Due to the toxicity of medications and subjectivity of taste panellists, as well as issues with hiring taste panellists and panel upkeep when working with unpleasant items, the use of sensory panellists in the industry is particularly challenging and troublesome. Furthermore, tests cannot be conducted on compounds that have not received FDA approval. As a result, the analytical taste-sensing multichannel sensory system known as the electronic tongue (also known as the artificial tongue or e-tongue) helps in reducing the number of samples that are ought to be assessed by trained sensory panels and also when the sample to be tasted is injurious or harmful for the concerned person. Therefore, e-tongue has advantages like lowering reliance on human panels. The working theory, the sensors used, and the pharmaceutical and food applications are covered, along with the major software used, difficulties, and future scope are also highlighted.

Chemometric approach in environmental pollution analysis: A critical review.

With the ever-increasing global population and industrialization, it has become a call of the hour to start taking care of the environment to balance the ecosystem. For this, effective monitoring and assessment are required, which involves collecting and measuring environmental details, temporal and spatial readings of environmental data, and parameters. However, assessment of the environment is very tedious as it includes monitoring target analytes, identifying their sources, and reporting, which invariably implies that detailed environmental monitoring would be an intricate and expensive process. The traditional protocols in environmental measures are often manual and time demanding, which makes it further difficult. Moreover, several changes also occur within the environment, which could be chemical, physical, or biological, and since these environmental impacts are often cumulative, it becomes difficult to measure an isolated system. Furthermore, the chances of skipping significant results and trends become high. Also, experimental data obtained from the environmental analysis are usually non-linear and multi-variant due to different associations among various contributing variables. Therefore, it is implied that accurate measurements and environment monitoring are not using traditional analytical protocols. Thus, the need for a chemometric approach in environmental pollution analysis becomes paramount due to the inherent limitations associated with the conventional approach of analyzing environmental datasets. Chemometrics has appeared as a potential technique, which enhances the particulars of the chemical datasets by using statistical and mathematical analysis methods to analyze chemical data beyond univariate analysis. Utilizing chemometrics to study the environmental data is a revolutionary idea as it helps identify the relationship between sources of contaminations, environmental drivers, and their impact on the environment. Hence, this review critically explores the concept of chemometrics and its application in environmental pollution analysis by briefly highlighting the idea of chemometrics, its types, applications, advantages, and limitations in the environmental domain. An attempt is also made to present future trends in applications of chemometrics in environmental pollution analysis.

Rapid and non-destructive freshness evaluation of squid by FTIR coupled with chemometric techniques.

BACKGROUND: Freshness is an important quality of squid with respect to determining the market price. The methods of evaluation of freshness fail to be widely used as a result of the lack of rapidity and quantitation. In the present study, a rapid and non-destructive quantification of squid freshness by Fourier transform infrared spectroscopy (FTIR) spectra combined with chemometric techniques was performed. RESULTS: The relatively linear content change of trimethylamine (TMA-N) and dimethylamine (DMA-N) of squid during storage at 4 °C indicated their feasibility as a freshness indicator, as also confirmed by sensory evaluation. The spectral changes were mainly caused by the degradation of proteins and the production of amines by two-dimensional infrared correlation spectroscopy, among which TMA-N, DMA-N and putrescine were the main amines. The successive projections algorithm (SPA) was employed to select the sensitive wavenumbers to freshness for modeling prediction including partial least-squares regression, support vector regression (SVR) and back-propagation artificial neural network. Generally, the SPA-SVR model of the selected characteristic wavenumber showed a higher prediction accuracy for DMA-N (R2 P  = 0.951; RMSEP  = 0.218), whereas both SPA-SVR (R2 P  = 0.929; RMSEP  = 2.602) and Full-SVR (R2 P  = 0.941; RMSEP  = 2.492) models had a higher predictive ability of TMA-N. CONCLUSION: The results of the present study demonstrate that FTIR spectroscopy coupled with multivariate calibration shows significant potential for the prediction of freshness in squid. © 2021 Society of Chemical Industry.

Hydrochemical characterization of groundwater quality using chemometric analysis and water quality indices in the foothills of Himalayas.

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic process. To analyse the spatial-temporal variation and pollution source identification and apportionment, the dataset was subjected to a globally acknowledged coherent technique using water quality indices and chemometric techniques (principal component analysis (PCA) and cluster analysis. The bulk of the samples tested were below the BIS's permissible levels. Groundwater samples from the pre- and post-monsoon seasons mostly contained the anions HCO- 3 > Cl- > SO2- 4 > NO- 3, while the primary cations were Ca2+ > Mg2+ > Na+ > K+. Groundwater was alkaline and hard at most of the sites. According to hydro-geochemical facies and relationships, Piper diagrams, and principal component analysis, weathering, dissolution, leaching, ion exchange, and evaporation were the key mechanisms influencing groundwater quality. The hydrochemical facies classified the groundwater samples into the Ca-Mg-HCO3 type. For all the sampling locations, PIG was determined to be 0.43, 0.52, 0.47, 0.48, 1.00, and 0.70; respectively. The majority of the test locations fell into the low to medium contamination zone, as determined by the groundwater pollution index (PIG) and contamination index. Three principal components, which together account for 93.8% of the total variance, were identified via PCA. The study's findings confirm the value of these statistical techniques in interpreting and understanding large datasets and offering reliable information to reduce the time and expense of programmes for monitoring and evaluating water quality.

Fluoride and nitrate in groundwater of rural habitations of semiarid region of northern Rajasthan, India: a hydrogeochemical, multivariate statistical, and human health risk assessment perspective.

In arid and semiarid regions, groundwater is required for the drinking, agriculture, and industrial activities due to scarcity of surface water. Groundwater contaminated with high concentrations of fluoride and nitrate can severely affect human health in these regions. Twenty-eight groundwater samples from rural habitations of Jhunjhunu district, Rajasthan, India, were collected in March 2018 and subjected to analysis for water quality parameters. Fluoride and nitrate concentrations in groundwater varied from 0 to 5.74 mg/L and 10.22-519.64 mg/L, respectively. Nitrate content of about 86% samples and fluoride content of about 54% exceeded the permissible limit of Bureau of Indian Standards (IS:10,500) as well as World Health Organization standards. All groundwater samples belonged to poor to unfit drinking water quality index. Principle component analysis elucidates the anthropogenic contribution to high nitrate concentrations observed in this area. Noncarcinogenic human health risk evaluated from high nitrate and fluoride in drinking water for children, men, and women points to the fact that noncarcinogenic risk is exceeding the allowable limit to human health. The predominating hydrochemical facies in the area is Na+-HCO3--Cl- followed by Na+-Mg2+-HCO3--Cl-. The Gibbs plot and bivariate ionic cross-plots suggest the noncarbonate weathering (rock dominance), evaporation dominance, and ion exchange process to be the predominating geochemical mechanisms governing the evolution of groundwater hydrogeochemistry. Giggenbach diagram shows the immature character, i.e., incomplete equilibration of the groundwater.

Groundwater hydro-geochemistry, quality, microbiology and human health risk assessment in semi-arid area of Rajasthan, India: a chemometric approach.

The present investigation focused on groundwater hydro-geochemistry of Alsisar block of Jhunjhunu district, India, aims on evaluating the quality of groundwater for drinking and irrigation purposes and assessing the human health risk from ingestion of groundwater. The groundwater of Alsisar block is neutral to alkaline, brackish and very hard in nature. Total dissolved solids, total hardness, Na+, Mg2+, HCO3-, F- and NO3- in majority of the groundwater samples were exceeding the World Health Organization and Bureau of Indian Standards recommended limits. The drinking water quality index ranged from 111.53 to 492.84. None of the sample belonged to excellent and good categories of drinking water quality. Fluoride varied from 0.018 to 4.176 mg L-1, and nitrate varied from 0.34 to 520.66 mg L-1 in groundwater. The non-carcinogenic risk assessment for children, men and women owing to ingestion of fluoride and nitrate-enriched groundwater indicates human health risks in the entire study area. Irrigation with groundwater of Alsisar block is liable to cause salinity and magnesium hazard to agricultural crops grown in the area. Source apportionment using principal component analysis suggests the geogenic origin of fluoride and anthropogenic origin of nitrate. Na+-Mg2+-Cl- followed by Na+-Mg2+-HCO3- are the predominant hydrochemical facies in the groundwater of Alsisar block. Silicate rock weathering, ion exchange and evaporation are the predominating processes governing ionic concentrations in the groundwater. Biochemical and molecular tests demonstrated the presence of Brevibacillus borstelensis strain DSM 6347 16s rRNA and Bacillus paramycoides strain MCCC 1A04098 16s rRNA in the groundwater of the area.

Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques.

Near infrared (NIR) spectroscopy with chemometric techniques was applied to discriminate the geographical origins of crude drugs (i.e., dried ripe fruits of Trichosanthes kirilowii) and prepared slices of Trichosanthis Fructus in this work. The crude drug samples (120 batches) from four growing regions (i.e., Shandong, Shanxi, Hebei, and Henan Provinces) were collected, dried, and used and the prepared slice samples (30 batches) were purchased from different drug stores. The raw NIR spectra were acquired and preprocessed with multiplicative scatter correction (MSC). Principal component analysis (PCA) was used to extract relevant information from the spectral data and gave visible cluster trends. Four different classification models, namely K-nearest neighbor (KNN), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and support vector machine-discriminant analysis (SVM-DA), were constructed and their performances were compared. The corresponding classification model parameters were optimized by cross-validation (CV). Among the four classification models, SVM-DA model was superior over the other models with a classification accuracy up to 100% for both the calibration set and the prediction set. The optimal SVM-DA model was achieved when C =100, γ = 0.00316, and the number of principal components (PCs) = 6. While PLS-DA model had the classification accuracy of 95% for the calibration set and 98% for the prediction set. The KNN model had a classification accuracy of 92% for the calibration set and 94% for prediction set. The non-linear classification method was superior to the linear ones. Generally, the results demonstrated that the crude drugs from different geographical origins and the crude drugs and prepared slices of Trichosanthis Fructus could be distinguished by NIR spectroscopy coupled with SVM-DA model rapidly, nondestructively, and reliably.

Comparison of Volatile Oil between the Fruits of Amomum villosum Lour. and Amomum villosum Lour. var. xanthioides T. L. Wu et Senjen Based on GC-MS and Chemometric Techniques.

Fructus Amomi (FA) is usually regarded as the dried ripe fruit of Amomum villosum Lour. (FAL) or Amomum villosum Lour. var. xanthioides T. L. Wu et Senjen (FALX.). However, FAL, which always has a much higher price because of its better quality, is often confused with FALX. in the market. As volatile oil is the main constituent of FA, a strategy combining gas chromatography-mass spectrometry (GC-MS) and chemometric approaches was applied to compare the chemical composition of FAL and FALX. The results showed that the oil yield of FAL was significantly higher than that of FALX. Total ion chromatography (TIC) showed that cis-nerolidol existed only in FALX. Bornyl acetate and camphor can be considered the most important volatile components in FAL and FALX., respectively. Moreover, hierarchical cluster analysis (HCA) and principal component analysis (PCA) successfully distinguished the chemical constituents of the volatile oils in FAL and FALX. Additionally, bornyl acetate, α-cadinol, linalool, ß-myrcene, camphor, d-limonene, terpinolene and borneol were selected as the potential markers for discriminating FAL and FALX. by partial least squares discrimination analysis (PLS-DA). In conclusion, this present study has developed a scientific approach to separate FAL and FALX. based on volatile oils, by GC-MS combined with chemometric techniques.

Rapid Determination of the Geographical Origin of Chinese Red Peppers (Zanthoxylum Bungeanum Maxim.) Based on Sensory Characteristics and Chemometric Techniques.

In this paper, principal component analysis (PCA), linear discriminant analysis (LDAp, artificial neural networks (ANN), and support vector machine (SVM) were applied to discriminate the geographical origin of Chinese red peppers (Zanthoxylum bungeanum Maxim.). The models based on color, smell and taste may discriminate quickly and effectively the geographical origin of Chinese red peppers from different regions, but the successful identification rates may vary with different kinds of parameters and chemometric methods. Among them, all models based on taste indexes showed an excellent ability to discriminate the geographical origin of Chinese red peppers with correct classifications of 100% for the training set and the 100% for test set. The present study provided a simple, efficient, inexpensive, practical and fast method to discriminate the geographical origin of Chinese red peppers from different regions, which was of great importance for both consumers and producers.

Characterization of Fatty Acid, Amino Acid and Volatile Compound Compositions and Bioactive Components of Seven Coffee (Coffea robusta) Cultivars Grown in Hainan Province, China.

Compositions of fatty acid, amino acids, and volatile compound were investigated in green coffee beans of seven cultivars of Coffea robusta grown in Hainan Province, China. The chlorogenic acids, trigonelline, caffeine, total lipid, and total protein contents as well as color parameters were measured. Chemometric techniques, principal component analysis (PCA), hierarchical cluster analysis (HCA), and analysis of one-way variance (ANOVA) were performed on the complete data set to reveal chemical differences among all cultivars and identify markers characteristic of a particular botanical origin of the coffee. The major fatty acids of coffee were linoleic acid, palmitic acid, oleic acid, and arachic acid. Leucine (0.84 g/100 g DW), lysine (0.63 g/100 g DW), and arginine (0.61 g/100 g DW) were the predominant essential amino acids (EAAs) in the coffee samples. Seventy-nine volatile compounds were identified and semi-quantified by HS-SPME/GC-MS. PCA of the complete data matrix demonstrated that there were significant differences among all cultivars, HCA supported the results of PCA and achieved a satisfactory classification performance.

Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review.

Honey, recognized for its diverse flavors and nutritional benefits, confronts challenges in maintaining authenticity and quality due to factors like adulteration and mislabelling. This review undertakes a comprehensive exploration of the utility of Near-Infrared (NIR) spectroscopy as a non-destructive analytical method for concurrently evaluating both honey quantity and authenticity. The primary purpose of this investigation is to delve into the various applications of NIR spectroscopy in honey analysis, with a specific focus on its capability to identify and quantify significant quality parameters such as sugar content, moisture levels, 5-HMF, and proline content. Results from the study underscore the effectiveness of NIR spectroscopy, especially when integrated with advanced chemometrics models. This combination not only facilitates quantification of diverse quality parameters but also enhances the classification of honey based on geographical and botanical origin. The technology emerges as a potent tool for detecting adulteration, addressing critical challenges in preserving the authenticity and quality of honey products. The impact of this critical analysis extends to shedding light on the current state, challenges, and future prospects of applying NIR spectroscopy in the honey industry. This analysis outlines the current challenges and future prospects of NIR spectroscopy in the honey industry. Emphasizing its potential to improve consumer confidence and food safety, the research has broader implications for authenticity and quality assurance in honey. Integrating NIR spectroscopy into industry practices could establish stronger quality control measures, benefiting both producers and consumers globally.

Rare Earth Element Variability in Italian Extra Virgin Olive Oils from Abruzzo Region.

Extra virgin olive oil is a food product from the Mediterranean area that is particularly and continuously experiencing to increasing instances of fraudulent geographical labeling. Therefore, origin protection must be improved, mainly based on its intrinsic chemical composition. This study aimed to perform a preliminary chemical characterization of Abruzzo extra virgin olive oils (EVOOs) using rare earth elements (REEs). REEs were evaluated in EVOO samples of different varieties produced in different geographical origins within the Abruzzo region (Italy) in three harvest years using ICP-MS chemometric techniques. Principal component, discriminant, and hierarchical cluster analyses were conducted to verify the influence of the variety, origin, and vintage of the REE composition. The results of a three-year study showed a uniform REE pattern and a strong correlation in most EVOOs, in particular for Y, La, Ce, and Nd. However, europium and erbium were also found in some oil samples. Compared with cultivar and origin, only the harvest year slightly influenced the REE composition, highlighting the interactions of the olive system with the climate and soil chemistry that could affect the multielement composition of EVOOs.

Assessment of potentially toxic elements in groundwater through interpolation, pollution indices, and chemometric techniques in Dehradun in Uttarakhand State.

Providing safe drinking water for the entire world's population is essential for ensuring sustainable development. The presence of harmful compounds in aquifers, majorly toxic elements, is a serious environmental concern around the globe. This research aimed to quantify for the initial period the amounts of toxic elements in freshwater in the Dehradun Industrial Region of Uttrakhand, India, as well as the associated health risks. The PTEs (potentially toxic elements) Fe, Cd, Mn, Cu, Cr and Pb, Zn, Ni is measured by AAS and compared to BIS and WHO requirements for drinking safety. The order of mean trace element values in all groundwater samples were determined as Fe > Zn > Cu > Ni > Co > Cd > Pb. HPI was discovered to be higher than high class during the research period (HPI > 30), but under the severe contamination criterion of 100. Iron's MI and PI values were consistently over the threshold limit during the research period, and certain toxic elements were discovered exceptionally near the threshold limit, indicating a severe future influence on groundwater quality. According to PCA (principal component analysis), CM (correlation matrix), and potential health hazard, maximum levels of toxic elements in groundwater in the Dehradun region are attributed to land use patterns, anthropogenic activity, industrial activity, fertilizer and pesticide leaching, and residential waste into the aquifer system. The findings of this study could aid local planners and policymakers in preventing health risks from contaminated aquifers through the deployment of suitable monitoring and mitigation measures.

Chemometric techniques in oil spill identification: A case study in Dalian 7.16 oil spill accident of China.

Chemometric methods have unique advantages regarding comprehensive consideration of multiple parameters and the classification of samples or variables. Classification of oil spill sources was carried out by using chemometric techniques, such as Repeatability Limit, hierarchical cluster analysis (HCA), Student's t-test and Principal component analysis (PCA) Biplot. In addition, this paper takes the fingerprint identification of a Dalian "7.16″ oil spill accident as an example to illustrate the effectiveness of chemometric techniques in oil identification. PCA scores plot (explaining 82.77% of variance accounted for three PCs) showed that samples belong to four clusters and result of HCA method further confirmed that. The residual oil in Jinshatan Beach and Haibei Square may be caused by the explosion of Dalian "7-16" oil pipeline accident. The use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution. The results will be of great significance to improve the accuracy and efficiency of oil spill identification based on oil fingerprint.

Persistent organic pollutants (POPs) - QSPR classification models by means of Machine learning strategies.

Persistent Organic pollutants (POPs) are toxic chemicals with a shallow degradation rate and global negative impact. Their physicochemical is combined with the complex effects of long-term POPs accumulation in the environment and transport function through the food chain. That is why POPs have been linked to adverse effects on human health and animals. They circulate globally via different environmental pathways, and could be detected in regions far from their source of origin. The primary goal of the present study is to carry out classification of various representatives of POPs using different theoretical descriptors (molecular, structural) to develop quantitative structure-properties relationship (QSPR) models for predicting important properties POPs. Multivariate statistical methods such as hierarchical cluster analysis, principal components analysis and self-organizing maps were applied to reach excellent partitioning of 149 representatives of POPs into 4 classes using ten most appropriate descriptors (out of 63) defined by variable reduction procedure. The predictive capabilities of the defined classes could be applied as a pattern recognition for new and unidentified POPs, based only on structural properties that similar molecules may have. The additional self-organizing maps technique made it possible to visualize the feature-space and investigate possible patterns and similarities between POPs molecules. It contributes to confirmation of the proper classification into four classes. Based on SOM results, the effect of each variable and pattern formation has been presented.

Kohonen Artificial Neural Network and Multivariate Analysis in the Identification of Proteome Changes during Early and Long Aging of Bovine Longissimus dorsi Muscle Using SWATH Mass Spectrometry.

To study proteomic changes involved in tenderization of Longissimus dorsi, Charolais heifers and bulls muscles were sampled after early and long aging (12 or 26 days). Sensory evaluation and instrumental tenderness measurement were performed. Proteins were analyzed by gel-free proteomics. By pattern recognition (principal component analysis and Kohonen's self-organizing maps) and classification (partial least squares-discriminant analysis) tools, 58 and 86 dysregulated proteins were detected after 12 and 26 days of aging, respectively. Tenderness was positively correlated mainly with metabolic enzymes (PYGM, PGAM2, TPI1, PGK1, and PFKM) and negatively with keratins. Downregulation in hemoglobin subunits and carbonic anhydrase 3 levels was relevant after 12 days of aging, while mimecan and collagen chains levels were reduced after 26 days of aging. Bioinformatics indicated that aging involves a prevalence of metabolic pathways after late and long periods. These findings provide a deeper understanding of changes involved in aging of beef and indicate a powerful method for future proteomics studies.

Towards Raman-Based Screening of Acute Lymphoblastic Leukemia-Type B (B-ALL) Subtypes.

Acute lymphoblastic leukemia (ALL) is the most common type of malignant neoplasms in the pediatric population. B-cell precursor ALLs (BCP-ALLs) are derived from the progenitors of B lymphocytes. Traditionally, risk factors stratifying therapy in ALL patients included age at diagnosis, initial leukocytosis, and the response to chemotherapy. Currently, treatment intensity is modified according to the presence of specific gene alterations in the leukemic genome. Raman imaging is a promising diagnostic tool, which enables the molecular characterization of cells and differentiation of subtypes of leukemia in clinical samples. This study aimed to characterize and distinguish cells isolated from the bone marrow of patients suffering from three subtypes of BCP-ALL, defined by gene rearrangements, i.e., BCR-ABL1 (Philadelphia-positive, t(9;22)), TEL-AML1 (t(12;21)) and TCF3-PBX1 (t(1;19)), using single-cell Raman imaging combined with multivariate statistical analysis. Spectra collected from clinical samples were compared with single-cell spectra of B-cells collected from healthy donors, constituting the control group. We demonstrated that Raman spectra of normal B cells strongly differ from spectra of their malignant counterparts, especially in the intensity of bands, which can be assigned to nucleic acids. We also showed that the identification of leukemia subtypes could be automated with the use of chemometric methods. Results prove the clinical suitability of Raman imaging for the identification of spectroscopic markers characterizing leukemia cells.

A new indexing approach for evaluating heavy metal contamination in groundwater.

Three indexing methods, namely heavy metal pollution index (HPI), contamination index (Cd) and heavy metal evaluation index (HEI), are commonly used for heavy metal evaluation in groundwater. These methods have several limitations. In HPI, 14 out of 15 groundwater samples collected in the study area of Nalagarh valley, Himachal Pradesh, India qualify for drinking purposes with their values varying between 10.73 and 107.50 (critical limit = 100), while in Cd, the same number of samples (>90%) are rejected as their values (Cd = 1.31-37.87) exceed the critical limit of 3. HEI varies from 10.31 to 46.87 with a mean of 26.06, but since it does not have a defined critical limit, quality assessment depends on worker's discretion. It thus becomes very confusing as to which indexing method to use. To overcome this dilemma, a very simple indexing method called 'heavy metal contamination index (HCI)' has been developed on the basis of assigning weight to each heavy metal parameter. A new classification system with six distinct water classes of different uses too has been proposed considering the regulatory limits, human health risk and toxicity of the violator parameters. Regression analysis confirms that HCI has larger number of significantly correlated key parameters compared to the other three indices. Chemometric techniques confirm that Cr, Cu, Fe, Mn and Zn are derived from lithogenic inputs and As, Cd, Ni and Pb from anthropogenic sources. HCI when integrated with Cluster Analysis gives the best possible results in identifying factors that influence the various water classes.