Mid infrared spectroscopy and chemometrics as tools for the classification of roasted coffees by cup quality.

Sensory (cup) analysis is a reliable methodology for green coffee quality evaluation, but faces barriers when applied to commercial roasted coffees due to lack of information on roasting conditions. The aim of this study was to examine the potential of mid-infrared spectroscopy for predicting cup quality of arabica coffees of different roasting degrees. PCA analysis showed separation of arabica and robusta. A two-level PLS-DA Hierarchical strategy was employed, with coffee being classified as high or low quality in the first level and then separated according to cup quality in the second level. Validation results showed that the second level models exhibited 100% sensitivity and specificity in the training sets. For the test set, sensitivity ranged from 67% (rio zona) to 100% (soft) while specificity ranged from 71% (rio) to 100% (rioysh, hard). Thus, the proposed method can be used for the quality evaluation of arabica coffees regardless of roasting conditions.

Performance review of a fast HPLC-UV method for the quantification of chlorogenic acids in green coffee bean extracts.

The aim of this study was to test the performance of a HPLC method, designated for rapid quantification of chlorogenic acids (CGA) in green coffee extract (GCE). The precision statistics associated with the method were assessed using three independent laboratories with five samples analyzed in triplicate. Seven main CGA isomers (3-CQA, 5-CQA, 4-CQA, 5-FQA, 3,4-diCQA, 3,5-diCQA and 4,5-diCQA) were quantified. The concentration of total CGA in the samples varied from 32.24% to 52.65% w/w. The repeatability and reproducibility standard deviations for the determination of individual isomers varied, respectively, from 0.01 to 0.28 and 0.05-1.59. The repeatability and reproducibility standard deviations of the calculated total CGA, corresponding to the sum of the seven main CGA isomers, varied respectively, from 0.17 to 0.58 and 0.55-2.01. The fast HPLC method evaluated in this study was considered precise and appropriate for the determination of CGA in GCE.

Fourier transform infrared spectroscopy and near infrared spectroscopy for the quantification of defects in roasted coffees.

The coffee strip-picking harvesting method, preferred in Brazil, results in high percentages of immature and overripe beans, as the fruits in a single tree branch do not reach ripeness at the same time. This practice, together with inappropriate processing and storage conditions, contribute to the production of high amounts of defective coffee beans in Brazil, which upon roasting will impart negative sensory aspects to the beverage. Therefore, the development of analytical methodologies that will enable the discrimination and quantification of defective and non-defective coffees after roasting is rather desirable. Given that infrared spectroscopy has been successfully applied to coffee analysis, the objective of this work was to evaluate and to compare the performances of Fourier transform infrared (FTIR) and near infrared (NIR) spectroscopies for the quantification of defective beans in roasted coffees. Defective and non-defective Arabica coffee beans were manually selected, roasted, ground and sieved. Mixtures of defective and non-defective roasted and ground coffees were produced and analyzed, with % defects ranging from 0% to 30%. FTIR and NIR spectra were recorded, respectively, within a range of 3100-800 cm(-1) and 1200-2400 nm and submitted to mathematical processing. Quantitative models were developed by partial least squares regression (PLSR). Excellent predictive results were obtained indicating that defective coffees could be satisfactorily quantified. The correlation coefficients and the root mean squared errors of validation for the FTIR and NIR models developed to quantify the amount of defective roasted coffees mixed with non-defective ones were, respectively, as high as 0.891 and as low as 0.032, and as high as 0.953 and as low as 0.026. A comparison between the two techniques indicated that NIR provided more robust models.

Application of elastic net and infrared spectroscopy in the discrimination between defective and non-defective roasted coffees.

The quality of the coffee beverage is negatively affected by the presence of defective coffee beans and its evaluation still relies on highly subjective sensory panels. To tackle the problem of subjectivity, sophisticated analytical techniques have been developed and have been shown capable of discriminating defective from non-defective coffees after roasting. However, these techniques are not adequate for routine analysis, for they are laborious (sample preparation) and time consuming, and reliable, simpler and faster techniques need to be developed for such purpose. Thus, it was the aim of this study to evaluate the performance of infrared spectroscopic methods, namely FTIR and NIR, for the discrimination of roasted defective and non-defective coffees, employing a novel statistical approach. The classification models based on Elastic Net exhibited high percentage of correct classification, and the discriminant infrared spectra variables extracted provided a good interpretation of the models. The discrimination of defective and non-defective beans was associated with main chemical descriptors of coffee, such as carbohydrates, proteins/amino acids, lipids, caffeine and chlorogenic acids.

Development of a gas chromatography-mass spectrometry method for the quantification of glucaric Acid derivatives in beverage substrates.

A gas chromatography-mass spectrometry (GC-MS) method using the standard addition methodology was developed for the determination of glucuronolactone (GL) and glucuronic acid (DGuA) in four beverages categorized as detoxification, recovery, or energy drinks. The method features a precolumn derivatization step with a combination of BSTFA (N,O-bis(trimethylsilyl)trifluoroacetamide) and TMCS (trimethylchlorosilane) to silylate the analytes. The sample pretreatment required no extraction, filtration, or reduction step prior to the injection. The quantification of the analytes was performed using a five-point standard addition protocol. The proposed method presented excellent intraday precision (%RSD < 10) and linearity for GL calibration curves (correlation coefficients > 0.995) and acceptable linearity for DGuA calibration curves (correlation coefficients > 0.97). The estimated limits of detection (LOD) and quantification (LOQ) for GL ranged from 0.006 ppm to 0.14 ppm, and 0.02 ppm to 0.47 ppm, respectively. The estimated LOD and LOQ for DGuA determination ranged, respectively, from 0.06 ppm to 1.1 ppm and 0.2 ppm to 3.8 ppm. The results demonstrated that the method should be regarded as a reliable alternative to the simultaneous determination of GL and DGuA.

Differentiation of cancer cells in two-dimensional and three-dimensional breast cancer models by Raman spectroscopy.

We demonstrate the first application of Raman spectroscopy in diagnosing nonmalignant, premalignant, malignant, and metastatic stages of breast cancer in a three-dimensional (3-D) cell culture model that closely mimics an in vivo environment. Comprehensive study comparing classification in two-dimensional (2-D) and 3-D cell models was performed using statistical methods composed of principal component analysis for exploratory analysis and outlier removal, partial least squares discriminant analysis, and elastic net regularized regression for classification. Our results show that Raman spectroscopy with an appropriate classification tool has excellent resolution to discriminate the four stages of breast cancer progression, with a near 100% accuracy for both 2-D and 3-D cell models. The diversity in chemical groups related to nucleic acids, proteins, and lipids, among other chemicals, were identified by appropriate peaks in the Raman spectra that correspond to the correct classification of the different stages of tumorigenesis model comprising of MCF10A, MCF10AneoT, MCF10CA1h, and MCF10CA1a cell lines. An explicit relationship between wavenumber and the stages of cancer progression was identified by the elastic net variable selection.

A hybrid FLIM-elastic net platform for label free profiling of breast cancer.

We report a label-free fluorescence lifetime profiling strategy to classify breast cancer cells, MCF10CA1h (malignant), MCF10A (nonmalignant), and MCF10AneoT (premalignant) in different stages of malignancy. Fluorescence Lifetime Imaging Microscopy (FLIM) was used to record the lifetime of autofluorescence of endogenous flavin in MCF10 cells in different stages of malignancy. Predominant differences in lifetimes ascertained by multi-exponential fitting curves can be attributed to the different forms of flavin protein; flavin mononucleotide (FMN), free flavin adenine dinucleotide (FAD), semiquinone, and bound FAD. A lifetime map of the metabolite was derived from the contribution of the lifetime of each metabolite by iterative reconvolution fitting of the Time Correlated Single Photon Counting (TCSPC) decay curves. Lifetime maps were constructed by mapping the average lifetime values pixel by pixel using MATLAB. The FLIM image (150 × 150 pixels) of each cell was extracted, resized and centered into 100 × 100 pixels using the nearest neighbor algorithm. Principal Component Analysis (PCA) in conjunction with Elastic net Analysis (EnA) was then used to classify the different stages of MCF10 cell lines based on average lifetime values. The EnA model provided an excellent classification of the cells at different stages of tumorigenesis yielding 100% accuracy.

Surface-enhanced Raman spectroscopy applied to food safety.

Surface-enhanced Raman spectroscopy (SERS) is an advanced Raman technique that enhances the vibrational spectrum of molecules adsorbed on or in the vicinity of metal particles and/or surfaces. Because of its readiness, sensitivity, and minimum sample preparation requirements, SERS is being considered as a powerful technique for food inspection. Key aspects of food-safety assurance, spectroscopy methods, and SERS are briefly discussed in an extended introduction of this review. The recent and potential advances in SERS are highlighted in sections that deal with the (a) detection of food-borne pathogenic microorganisms and (b) the detection of food contaminants and adulteration, concentrated specifically on antibiotics, drugs, hormones, melamine, and pesticides. This review provides an outlook of the work done and a perspective on the future directions of SERS as a reliable tool for food-safety assessment.

Evaluation of the potential of FTIR and chemometrics for separation between defective and non-defective coffees.

The objective of this work was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) for the discrimination of defective and non-defective coffee beans. Defective (black, immature and sour) and non-defective Arabica coffee beans were submitted to FTIR analysis by transmittance readings employing KBr discs and reflectance readings employing attenuated total reflectance (ATR) and diffuse reflectance (DR) accessories. Multivariate statistical analysis (PCA, clusters) was performed in order to verify the possibility of discrimination between defective and non-defective coffee samples. A clear separation between defective and non-defective coffee beans was observed, based on both PCA and cluster analysis of the reflectance spectra (ATR and DR accessories) and of the first derivatives of the transmittance spectra (KBr discs). Such results indicate that FTIR analysis has the potential for the development of a fast and reliable analytical methodology for the discrimination between defective and non-defective coffee beans.

Discrimination between immature and mature green coffees by attenuated total reflectance and diffuse reflectance Fourier transform infrared spectroscopy.

The objective of this work was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) in the characterization and discrimination between immature and mature or ripe coffee beans. Arabica coffee beans were submitted to FTIR analysis by reflectance readings employing attenuated total reflectance (ATR) and diffuse reflectance (DR) accessories. The obtained spectra were similar, but in general higher absorbance values were observed for nondefective beans in comparison to immature ones. Multivariate statistical analysis (principal component analysis, PCA, and agglomerative hierarchical clustering, AHC) was performed in order to verify the possibility of discrimination between immature and mature coffee samples. A clear separation between immature and mature coffees was observed based on AHC and PCA analyses of the normalized spectra obtained by employing both ATR and DR accessories. Linear discriminant analysis was employed for developing classification models, with recognition and prediction abilities of 100%. Such results showed that FTIR analysis presents potential for the development of a simple routine methodology for separation of immature and mature coffee beans. Practical Application: The ultimate goal of this research is to be able to propose improvements in the way immature coffee beans are separated from graded mature beans in coffee facilities (cooperatives and other coffee producer's associations). The results obtained herein point toward FTIR as a potential tool for the aimed improvements.