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Fourier transform Raman spectroscopy and chemometric tools have been used for exploratory analysis of pure corn and cassava starch samples and mixtures of both starches, as well as for the quantification of amylose content in corn and cassava starch samples. The exploratory analysis using principal component analysis shows that two natural groups of similar samples can be obtained, according to the amylose content, and consequently the botanical origins. The Raman band at 480 cm(-1), assigned to the ring vibration of starches, has the major contribution to the separation of the corn and cassava starch samples. This region was used as a marker to identify the presence of starch in different samples, as well as to characterize amylose and amylopectin. Two calibration models were developed based on partial least squares regression involving pure corn and cassava, and a third model with both starch samples was also built; the results were compared with the results of the standard colorimetric method. The samples were separated into two groups of calibration and validation by employing the Kennard-Stone algorithm and the optimum number of latent variables was chosen by the root mean square error of cross-validation obtained from the calibration set by internal validation (leave one out). The performance of each model was evaluated by the root mean square errors of calibration and prediction, and the results obtained indicate that Fourier transform Raman spectroscopy can be used for rapid determination of apparent amylose in starch samples with prediction errors similar to those of the standard method.

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Practical implementation of multivariate calibration models has been limited in several areas due to the requirement of appropriate development and validation to prove their performance to standardization agencies. Herein, a detailed description of the application of multivariate calibration based on partial least-squares regression models (PLSR) for the determination of soluble solids (BRIX), polarizable sugars (POL), and reducing sugars (RS) in sugar cane juice, based on near infrared spectroscopy (NIR), for the alcohol industries is presented. The development of the models, including variable selection and outlier elimination, and their validation by determination of figures of merit, such as accuracy, precision, sensitivity, analytical sensitivity, prediction intervals, and limits of detection and quantification, are described for a representative data set of 1381 sugar cane samples. Values estimated by PLSR are compared with appropriate reference methods, where the results indicated that the PLSR models can be used in the alcohol industry as an alternative to refractometry and lead clarification before polarization measurements (standard methods for BRIX and POL, respectively). For RS, the results of a titration reference method were compared with the PLSR estimates and also with an estimate based on BRIX and POL values, as actually used in the alcohol industry. The PLSR method presented a better agreement with the titration method. However, the results indicated that the RS estimates from both PLSR and those based on the BRIX and POL values, actually used, should be improved to a safe determination of RS.

The objective of this work was to determine liquid-liquid equilibrium data at (298.3 ( 0.2) K for systems of interest in biodiesel production, such systems being composed of vegetable oils + anhydrous ethanol + hexane. The measurements were performed using near-infrared spectroscopy (NIR) for quantification of the phases in equilibrium. The following vegetables oils were investigated: pretreated cottonseed oil, corn oil, canola oil, refined soybean oil, and degummed soybean oil. Initially, with the purpose of validating the NIR methodology, equilibrium data for the system composed of pretreated cottonseed oil (neutral) + commercial linoleic acid + anhydrous ethanol were determined at (298.2 ( 0.1) K by a conventional method based on acid-base titration and solvent evaporation and by the NIR method. The relative errors between the phase compositions determined by both analytical methods were 2.1 % for the alcoholic phase and 2.0 % for the oil phase. The mass balance errors for all investigated systems varied in the range from 0.03 % to 0.16 %, which indicates the good quality of the experimental data and the good performance of the NIR method. The experimental data were correlated using the NRTL model with an average global deviation of 0.70 %.

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Single molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to revolutionize quantitative analysis at ultralow concentrations (less than 1 nM). However, there are no established protocols to generalize the application of this technique in analytical chemistry. Here, a protocol for quantification at ultralow concentrations using SM-SERS is proposed. The approach aims to take advantage of the stochastic nature of the single-molecule regime to achieved lower limits of quantification (LOQ). Two emerging contaminants commonly found in aquatic environments, enrofloxacin (ENRO) and ciprofloxacin (CIPRO), were chosen as nonresonant molecular probes. The methodology involves a multivariate resolution curve fitting known as non-negative matrix factorization with alternating least-squares algorithm (NMF-ALS) to solve spectral overlaps. The key element of the quantification is to realize that, under SM-SERS conditions, the Raman intensity generated by a molecule adsorbed on a "hotspot" can be digitalized. Therefore, the number of SERS event counts (rather than SERS intensities) was shown to be proportional to the solution concentration. This allowed the determination of both ENRO and CIPRO with high accuracy and precision even at ultralow concentrations regime. The LOQ for both ENRO and CIPRO were achieved at 2.8 pM. The digital SERS protocol, suggested here, is a roadmap for the implementation of SM-SERS as a routine tool for quantification at ultralow concentrations.