J.P. Huvenne

Statistical tests for comparison of quantitative and qualitative models developed with near infrared spectral data

Near-infrared spectroscopy (NIRS) has been applied for both qualitative and quantitative evaluation of sugar beet. However, chemometrics methods are numerous and a choice criterion is sometime difficult to determine. In order to select the most accurate chemometrics method, statistical tests are developed. In the first part, quantitative models, which predict sucrose content of sugar beet, are compared. To realize a systematic study, 54 models are developed with different spectral pre-treatments (Standard Normal Variate (SNV), Detrending (D), first and second Derivative), different spectral ranges and different regression methods (Principal Component Regression (PCR), Partial Least Squares (PLS), Modified PLS (MPLS)). Analyze of variance and Fishers tests are computed to compare respectively bias and Standard Error of Prediction Corrected for bias (SEP(C)). The model developed with full spectra pre-treated by SNV, second derivative and MPLS methods gives accurate results: bias is 0.008 and SEP(C) is 0.097 g of sucrose per 100 g of sample on a concentration range between 14 and 21 g/100 g. In the second part, McNemars test is applied to compare the classification methods. The classifications are used with two data sets: the first data set concerns the disease resistance of sugar beet and the second deals with spectral differences between four spectrometers. The performances of four well-known classification methods are compared on the NIRS data: Linear Discriminant Analysis (LDA), K Nearest Neighbors method (KNN), Simple Modeling of Class Analogy (SIMCA) and Learning Vector Quantization neural network (LVQ) are computed. In this study, the most accurate method (SIMCA) has a prediction rate of 81.9% of good classification on the disease resistance determination and has 99.4% of good classification on the instrument data set.

Quantitative analysis of water-soluble vitamins by ATR-FTIR spectroscopy

HPLC and microbiology are the methods traditionally employed to control the vitamin content in food mixtures. However, considerations of cost, time of analysis per sample and complexities involved in the technique have hampered the acceptance of those methods for raw materials analysis. Fourier Transform Infrared (FTIR) spectroscopy has substantial potential as a quantitative quality control tool for the food industry. FTIR analysis methods are convenient, rapid, accurate, and in conjunction with Attenuated Total Reflectance (ATR) technology, simplify sample handling. The advantage of choosing FTIR as a quantitative technique lies in its ability to readily carry out multicomponent analysis in association with software such as Partial Least Squares (PLS) regression. Results presented here were obtained from water-soluble vitamins (B1, B2, B6 and Niacin) mixtures diluted into a glucose matrix without any chemical extraction.

Mid-infrared spectroscopy and chemometrics in corn starch classification

Abstract The authentication of food is a very important issue for both the consumer and the food industry at all levels of the food chain from raw materials to finished products. Corn starch can be used in a wide variety of food preparations such as bakery cream fillings, sauces, salad dressings, frozen foods etc. Many modifications are made to corn starch in connection with its use in agrofood. The value of the product increases with the degree of modification. Some chemical and physical tests have been devised to solve the problem of identifying these modifications but all the methods are time consuming and require skilled operators. We separate corn starches into groups related to their modification on the basis of the infrared spectra.

Determination of Sugars and Organic Acids in Fruit Juices by FT Mid-IR Investigation of Dry Extract

In an extension of the approach adopted by Meurens et al. for dispersive NIR spectroscopy, the principle of using dry extracts has been applied to FT-IR spectroscopy, to make use of three advantages: the rapidity of Fourier transform spectroscopy, the solvent elimination, and the better peak resolution in the mid-IR region. However, sampling appears more difficult in mid-IR than in NIR spectroscopy. The feasibility of quantitative analysis has, in a first step, been tested on synthetic samples before application to natural fruit juices. The performance of our dry extract method is reported in terms of spectroscopy as well as of multicomponent quantitative analysis of sugars and organic acids in fruit juices.

Self-Modeling Mixture Analysis Applied to FT-Raman Spectral Data of Hydrogen Peroxide Activation by Nitriles

In the analytical environment, spectral data resulting from analysis of samples often represent mixtures of several components. Extraction of information about pure components of these kinds of mixtures is a major problem, especially when reference spectra are not available or when unstable intermediates are formed. Self-modeling multivariate mixture analysis has been developed for this type of problem. In this paper two examples will be used to show the potential of this technique coupled with FT-Raman spectroscopy to elucidate reaction mechanisms and to follow in situ the kinetics of chemical transformations.

Standardisation of near-IR spectrometers using artificial neural networks

Abstract In analytical chemistry, the use of chemometrics on near-infrared data undergoes a major problem. Large increase of error prediction is observed when calibration equation developed on a first instrument is directly used on another one. Since many spectral differences between two spectrometers can occur, standardisation procedure has rapidly became a necessary step for a long-dated use of quantitative or qualitative models. An original neural network approach is proposed to correct spectral differences by modelling spectral response of an instrument from second one before using calibration equations. In this way, the consuming time recalibration step of the second spectrometer was avoided and initial error prediction level was retrieved.

In situ characterisation of peroxybenzimidic acid by FT-Raman and ATR/FTIR spectroscopy

Abstract To elucidate the mechanism of the reaction of benzonitrile and hydrogen peroxide in alkaline medium, an experimental study was carried out by FT-Raman and ATR/FTIR. We were able to identify the intermediate peroxycarboximidic acid by assigning number of characteristic vibrations like νCN or νOO.

In situ quantitative and kinetic study by Fourier transform Raman spectroscopy of reaction between nitriles and hydroperoxides

Abstract The reaction of nitrile with alkaline hydrogen peroxide has been studied kinetically by means of iodometry several times. We proposed to set up an in situ analytical method to follow the consumption of nitrile. We applied FT-Raman spectroscopy coupled with a partial least-squares quantitative method to the study the reactions of hydrogen peroxide or tert -butyl hydroperoxide on benzonitrile and acetonitrile. The results obtained led us to conclusions on the reactivity of the hydroperoxides and on the rate of consumption of nitrile. Our study made also clear that FT-Raman spectroscopy was a convenient tool for controlling industrial processes.