Kássio M. G. Lima

Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration.

The aim of this study was to evaluate near-infrared reflectance spectroscopy (NIR), and multivariate calibration potential as a rapid method to determinate anthocyanin content in intact fruit (açaí and palmitero-juçara). Several multivariate calibration techniques, including partial least squares (PLS), interval partial least squares, genetic algorithm, successive projections algorithm, and net analyte signal were compared and validated by establishing figures of merit. Suitable results were obtained with the PLS model (four latent variables and 5-point smoothing) with a detection limit of 6.2 g kg(-1), limit of quantification of 20.7 g kg(-1), accuracy estimated as root mean square error of prediction of 4.8 g kg(-1), mean selectivity of 0.79 g kg(-1), sensitivity of 5.04×10(-3) g kg(-1), precision of 27.8 g kg(-1), and signal-to-noise ratio of 1.04×10(-3) g kg(-1). These results suggest NIR spectroscopy and multivariate calibration can be effectively used to determine anthocyanin content in intact açaí and palmitero-juçara fruit.

Determination of quality attributes in wax jambu fruit using NIRS and PLS.

The aim of this work was to develop an analytical method to predict total anthocyanins content (TAC) and total phenolic compounds (TPC) in intact wax jambu fruit [Syzygium malaccense (L.) Merryl et Perry] using near-infrared spectroscopy (NIRS) and partial least squares (PLS). The estimation accuracy was based on parameters such as root mean square error of prediction (RMSEP), correlation coefficients [calibration (rc) and prediction (rp) set] and ratio of performance to deviation (RPD). TAC, rp = 0.98, RMSEP = 9.0 mg L(-1) and RPD = 5.19 were attained using second derivative pre-treatment. TPC, rp = 0.94, RMSEP = 22.18 (mg gallic acid equivalents (GAE)/100g) and RPD = 3.27 (excellent accuracy) were also obtained using second derivative pre-treatment. These findings suggest that the NIRS and PLS algorithms can be used to determine TCA and TPC in intact wax jambu fruit.

The use of near infrared spectroscopy and multivariate techniques to differentiate Escherichia coli and Salmonella Enteritidis inoculated into pulp juice

This study shows the application and usefulness of near infrared (NIR) transflectance spectra measurements in the identification and classification of Escherichia coli and Salmonella Enteritidis from commercial fruit pulp (pineapple). Principal component analysis (PCA), soft independent modeling of class analogy (SIMCA) analysis and partial least-squares discriminant analysis (PLS-DA) were used in the analysis. It was not possible to obtain total separation between the samples using PCA and SIMCA. PLS-DA presented good performance achieving prediction ability of 87.5% for E. coli and 88.3% for S. Enteritidis, respectively. For the best models, the sensitivity and specificity was 0.87 and 0.83 for PLS-DA with second derivative spectra. These results suggest that NIR spectroscopy and PLS-DA can be used to discriminate and detect bacteria in fruit pulp for modeling linear class boundaries.

Non-destructive detection of adulterated tablets of glibenclamide using NIR and solid-phase fluorescence spectroscopy and chemometric methods.

This study describes a method for non-destructive detection of adulterated glibenclamide tablets. This method uses near infrared spectroscopy (NIRS) and fluorescence spectroscopy along with chemometric tools such as Soft Independent Modeling of Class Analogy (SIMCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Unfolded Partial Least Squares with Discriminant Analysis (UPLS-DA). Both brand name (Daonil) and generic glibenclamide tablets were used for analysis. The levels of glibenclamide in each type of tablet were evaluated by derivative spectrophotometry in the ultraviolet region. The results obtained from the NIR and fluorescence spectroscopy along with those obtained from multivariate data classification show that this combined technique is an effective way to detect adulteration in drugs for the treatment of diabetes. In the future, this method may be extended to detect different types of counterfeit medications.

Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR‐FTIR spectroscopy coupled with variable selection methods

Ovarian cancer is a solid tumor and a leading cause of mortality. Diagnostic tools for the detection of early stage (stage I) ovarian cancer are urgently needed. For this purpose, attenuated total reflection Fourier‐transform infrared spectroscopy (ATR‐FTIR) coupled with variable selection methods, successive projection algorithm or genetic algorithm (GA) combined with linear discriminant analysis (LDA), were employed to identify spectral biomarkers in blood plasma or serum samples for accurate diagnosis of different stages of ovarian cancer, histological type and segregation based on age. Three spectral datasets (stage I vs. stage II–IV; serous vs. non‐serous carcinoma; and, ≤60 years vs. >60 years) were processed: sensitivity and specificity required for real‐world diagnosis of ovarian cancer was achieved. Toward segregating stage I vs. stage II–IV, sensitivity and specificity (plasma blood) of 100% was achieved using a GA‐LDA model with 33 wavenumbers. For serous vs. non‐serous category (plasma blood), the sensitivity and specificity levels, using 29 wavenumbers by GA‐LDA, were remarkable (up to 94%). For ≤60 years and >60 years categories (plasma blood), the sensitivity and specificity, using 42 wavenumbers by GA‐LDA, gave complete accuracy (100%). For serum samples, sensitivity and specificity results gave relatively high accuracy (up to 91.6% stage I vs. stage II–IV; up to 93.0% serous vs. non‐serous; and, up to 96.0% ≤60 years vs. >60 years) using several wavenumbers. These findings justify a prospective population‐based assessment of biomarkers signatures using ATR‐FTIR spectroscopy as a screening tool for stage of ovarian cancer.

Predicting soluble solid content in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit using near-infrared spectroscopy and chemometrics

The aim of this study was to evaluate the potential of near-infrared reflectance spectroscopy (NIR) as a rapid and non-destructive method to determine soluble solid content (SSC) in intact jaboticaba [Myrciaria jaboticaba (Vell.) O. Berg] fruit. Multivariate calibration techniques were compared with pre-processed data and variable selection algorithms, such as partial least squares (PLS), interval partial least squares (iPLS), a genetic algorithm (GA), a successive projections algorithm (SPA) and nonlinear techniques (BP-ANN, back propagation of artificial neural networks; LS-SVM, least squares support vector machine) were applied to building the calibration models. The PLS model produced prediction accuracy (R(2)=0.71, RMSEP=1.33 °Brix, and RPD=1.65) while the BP-ANN model (R(2)=0.68, RMSEM=1.20 °Brix, and RPD=1.83) and LS-SVM models achieved lower performance metrics (R(2)=0.44, RMSEP=1.89 °Brix, and RPD=1.16). This study was the first attempt to use NIR spectroscopy as a non-destructive method to determine SSC jaboticaba fruit.

Classification of cervical cytology for human papilloma virus (HPV) infection using biospectroscopy and variable selection techniques

Cervical cancer is the second most common cancer in women worldwide. We set out to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy combined with principal component analysis–linear discriminant analysis (PCA–LDA) or, variable selection techniques employing successive projection algorithm or genetic algorithm (GA) could classify cervical cytology according to human papilloma virus (HPV) infection [high-risk (hr) vs. low-risk (lr)]. Histopathological categories for squamous intraepithelial lesion (SIL) were segregated into grades (low-grade vs. high-grade) of cervical intraepithelial neoplasia (CIN) expressing different HPV infection (16/18, 31/35 or HPV Others). Risk assessment for HPV infection was investigated using age (≤29 years vs. >30 years) as the distinguishing factor. Liquid-based cytology (LBC) samples (n = 350) were collected and interrogated employing ATR-FTIR spectroscopy. Accuracy test results including sensitivity and specificity were determined. Sensitivity in hrHPV category was high (≈87%) using a GA–LDA model with 28 wavenumbers. Sensitivity and specificity results for >30 years for HPV, using 28 wavenumbers by GA–LDA, were 70% and 67%, respectively. For normal cervical cytology, accuracy results for ≤29 years and >30 years were high (up to 81%) using a GA–LDA model with 27 variables. For the low-grade cervical cytology dataset, 83% specificity for ≤29 years was achieved using a GA–LDA model with 33 wavenumbers. HPV16/18 vs. HPV31/35 vs. HPV Others were segregated with 85% sensitivity employing a GA–LDA model with 33 wavenumbers. We show that ATR-FTIR spectroscopy of cervical cytology combined with variable selection techniques is a powerful tool for HPV classification, which would have important implications for the triaging of patients.

Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

Significance Vibrational spectroscopy is an ideal technique for analysis of biofluids, as it provides a “spectral fingerprint” of all of the molecules present within a biological sample, thus generating a holistic picture of the sample’s status. Neurodegenerative diseases lack early and accurate diagnosis, and tests currently used for their detection are either invasive or expensive and time-consuming. This study used blood plasma to diagnose and differentiate various neurodegenerative diseases; the achieved sensitivities and specificities are equal to, or even higher than, the ones obtained by clinical/molecular methods. Herein, we show that spectroscopy could provide a simple and robust diagnostic test. Additional work should include asymptomatic individuals for an early screening test and exploration of neurodegenerative diseases at all stages of severity. The progressive aging of the world’s population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer’s disease (AD; n = 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein ε4 genotype (APOE ε4) information, increased to 86% when individuals carried one or two alleles of ε4, and to 72% sensitivity and 77% specificity when individuals did not carry ε4 alleles. Early AD cases (n = 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB; n = 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD; n = 30), Parkinson’s disease (PD; n = 32), and progressive supranuclear palsy (PSP; n = 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias.

FTIR microspectroscopy coupled with variable selection methods for the identification of flunitrazepam in necrophagous flies

The detection and identification of a drug in a corpse through the analysis of fly larvae feeding on the body by spectroscopic techniques promises to be of great value, because of their sensitivity, promptness, low cost and simplicity. Therefore, the purpose of this study was to develop a method based on Fourier-transform infrared (FTIR) microscopy to identify and discriminate flunitrazepam in necrophagous flies (Chrysomya megacephala, Chrysomya albiceps and Cochliomyia macellaria) as a non-invasive and non-destructive technique. Thirty-two Wistar mice were divided into two groups of sixteen and supplemented in two categories: group 1 – ethanol; and group 2 – standard flunitrazepam at a dose of 2 mg kg−1. Spectra from the larvae samples were analyzed by principal component analysis-linear discriminant analysis (PCA-LDA), and variable selection techniques such as successive projection algorithm (SPA-LDA) and genetic algorithm (GA-LDA) to determine if control versus flunitrazepam could be segregated. In addition, the multivariate classification accuracy results were tested based on sensitivity, specificity, positive (or precision) and negative predictive values, Youden index, and positive and negative likelihood ratios. For control vs. flunitrazepam category, the sensitivity and specificity levels, using 46 wavenumbers by SPA-LDA, gave relatively good accuracy (up to 82.3% control vs. flunitrazepam). The resulting GA-LDA model also successfully classified both classes with respect to the main biochemical alterations induced by flunitrazepam using only 40 wavenumbers (up to 88.2% control vs. flunitrazepam). Compared to classical methods, this new approach could represent an alternative and an innovative tool for faster and cheaper evaluation in entomotoxicology.

Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration

This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.