Fast Selection of Spectral Variables with B-Spline Compression

The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables hopefully can be reduced, by using either projection techniques or selection methods; the latter allow for the interpretation of the selected variables. Since the optimal approach of testing all possible subsets of variables with the prediction model is intractable, an incremental selection approach using a nonparametric statistics is a good option, as it avoids the computationally intensive use of the model itself. It has two drawbacks however: the number of groups of variables to test is still huge, and colinearities can make the results unstable. To overcome these limitations, this paper presents a method to select groups of spectral variables. It consists in a forward-backward procedure applied to the coefficients of a B-Spline representation of the spectra. The criterion used in the forward-backward procedure is the mutual information, allowing to find nonlinear dependencies between variables, on the contrary of the generally used correlation. The spline representation is used to get interpretability of the results, as groups of consecutive spectral variables will be selected. The experiments conducted on NIR spectra from fescue grass and diesel fuels show that the method provides clearly identified groups of selected variables, making interpretation easy, while keeping a low computational load. The prediction performances obtained using the selected coefficients are higher than those obtained by the same method applied directly to the original variables and similar to those obtained using traditional models, although using significantly less spectral variables

Analyse chimique immédiate des matières grasses par capteur spectrophotométrique

Les techniques spectrophotométriques permettent de déterminer instantanément la concentration de différentes espèces chimiques sans préparation ni transformation des échantillons. L’application de ces techniques et notamment de la spectroscopie proche infrarouge en contrôle de qualité des industries alimentaires est développée par de nombreux laboratoires et entreprises depuis plus de vingt ans. Les performances analytiques de nouveaux spectrophotomètres en mesure tant d’émission fluorescente et Raman que d’absorption proche infrarouge sont présentées ici, afin de montrer comment ces instruments répondent aux besoins d’analyse rapide et de contrôle de qualité en ligne dans le domaine oléicole. Dotés de fibres optiques et de détecteurs à couplage de charge, les spectrophotomètres de la dernière génération appelés capteurs spectrophotométriques ou spectrocapteurs s’avèrent convenir à l’analyse chimique immédiate et non destructive des matières grasses avec la détermination de paramètres comme les teneurs en lipides totaux et en acides gras libres pour la spectrométrie proche infrarouge, les teneurs en différents polyphénols et en chlorophylle pour la spectrofluorimétrie et les teneurs en acides gras à doubles liaisons en configuration trans pour la spectrométrie Raman

[Immediate chemical analysis of fats by spectrophotometric sensors]

The spectrophotometric techniques allow to determine instantaneously the concentration of chemical species without sample preparation and transformation, The application of these techniques, notably the near infrared spectroscopy in quality control of food industry has been developed by numerous laboratories and companies for more than twenty years. The analytical performances of new spectrophotometers in measurement of fluorescent and Roman emission as well as near infrared absorption ore presented here in order to explain how these instruments answer the needs of fast analysis and on-line quality control in the oils and fats domain. Equipped with fiber optics and coupled charge device detectors, the lost generation of spectrophotometers named spectrophotometric sensors or spectrosensors suit the immediate and non-destructive chemical analysis of fats and oils with the determination of parameters such as total lipids, free fatty acids for the near infrared spectrometry, polyphenols and chlorophyl for the spectrofluorimetry and trans fatty acids for the Roman spectroscopy

Chemical discrimination of arabica and robusta coffees by Fourier transform Raman spectroscopy.

This article deals with the potential of Fourier transform (FT) Raman spectroscopy in discrimination of botanical species of green and roasted coffees. There are two species of commercial importance: Coffea arabica (arabica) and Coffea canephora (robusta). It is recognized that they differ in their lipid fraction, especially in the content of the diterpene kahweol, which is present at 0.1-0.3% dry matter basis in arabica beans and only in traces (<0.01%) in robusta. The visual examination of the Raman spectra of the lipid fraction extracted from arabica, robusta and liberica samples shows differences in the mid-wavenumbers region: arabica spectra have two characteristic scattering bands at 1567 and 1478 cm(-1). The spectrum of the pure kahweol shows the same bands. Principal component analysis is applied to the spectra and reveals clustering according to the coffee species. The first principal component (PC1) explains 93% of the spectral variation and corresponds to the kahweol concentration. Using the PC1 score plot, two groups of arabica can be distinguished as follows: one group with high kahweol content and another group with low kahweol content. The first group includes samples coming from Kenya and Jamaica; the second group includes samples from Australia. The main difference between these coffees is that those from Kenya and Jamaica are well-known for growing at a high altitude whereas those ones from Australia are grown at a low altitude. To our knowledge, the application of Raman spectroscopy has never been used in coffee analysis

Quantitative analysis of individual sugars and acids in orange juices by near-infrared spectroscopy of dry extract

The combination of near-infrared spectroscopy (near-IR) and multivariate calibration for determination of glucose, fructose, sucrose, and citric and malic acids in orange juices was investigated. The concentrations of these components analyzed by enzymatic assays were considered as references relative to near-LR spectroscopy. Dry extract spectra of 218 orange juice samples were recorded in transmission mode between 1100 and 2500 nm. The original near-LR spectral data could be improved by mathematical pretreatments such as derivative transformations or multiplicative signal correction. Stepwise multiple Linear regression (SMLR) and partial least-squares regression (PLSR) were used to create calibration models relating chemical reference values to spectral data. The prediction ability of calibration models is acceptable in comparison with the reference methods. The calibration and validation results provided by PLS-1 calibration models are slightly better than those obtained with SMLR calibration models

Extractive sampling methods to improve the sensitivity of FTIR spectroscopy in analysis of aqueous liquids

Several methods of extractive sampling based on solvent elimination have been used to determine the concentration of different substances such as a sugar and a volatile organochloride in aqueous solutions. A dry extract technique based on the use of thin microporous polyethylene films as sample support has been evaluated in comparison with the direct transmission measurement through the liquid samples. Afterwards the Attenuated Total Reflection (ATR) on ZnSe and Ge crystals with a polymer coating has also been tested. The highest signal-to-noise ratio was obtained with the dry extract technique for the determination of a non volatile sugar. On the other hand, the best signal, lowering the detection limits to the low ppm concentrations, was obtained with the liquid extractive sampling (LES) method using a polymer coating on an ATR crystal for the determination of organochlorides