Preprocessing NIR Spectra for Aquaphotomics

Abstract

Even though NIR spectroscopy is based on the Beer–Lambert law, which clearly relates the concentration of the absorbing elements with the absorbance, the measured spectra are subject to spurious signals, such as additive and multiplicative effects. The use of NIR spectra, therefore, requires a preprocessing step. This article reviews the main preprocessing methods in the light of aquaphotomics. Simple methods for visualizing the spectra are proposed in order to guide the user in the choice of the best preprocessing. The most common chemometrics preprocessing are presented and illustrated by three real datasets. Some preprocessing aims to produce a spectrum as close as possible to the absorbance that would have been measured under ideal conditions and is very useful for the establishment of an aquagram. Others, dedicated to the improvement of the resolution of the spectra, are very useful for the identification of the peaks. Finally, special attention is given to the problem of reducing multiplicative effects and to the potential pitfalls of some very popular methods in chemometrics. Alternatives proposed in recent papers are presented.

Development of ANN Models for Prediction of Physical and Chemical Characteristics of Oil-in-Aqueous Plant Extract Emulsions Using Near-Infrared Spectroscopy

“…PCA significantly reduces the number of variables; it can account for the most variability, beginning with the first factor containing the greatest variance explained and ending with the final factor with the least variance explained [26]. Besides the fact that PCA enables observation of the variety between spectra by evaluating the scores, studying the loadings derived from PCA also provides insight into the source of the observed variability [42]. In this study, PCA was used to compare the raw NIR spectra of the produced oil-in-aqueous oregano/rosemary extract emulsions.…”

“…Savitzky-Golay (SG) smoothing is a popular preprocessing approach that successfully eliminates disturbances such as baseline drift and tilt [47]. Smoothing parameters include the polynomial degree, polynomial derivative order, and number of smoothing points [42,48]. Multiplicative signal correction (MSC) is a technique used when the two principal impacts are present in the samples.…”

Aquaphotomics—Exploring Water Molecular Systems in Nature

“…Out of the 21 published articles, 3 were reviews [ 2 , 3 , 4 ] and 18 were original research papers [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], that can be roughly separated into 6 categories ( Figure 3 ).…”

“…The majority of the articles, approximately one third, belongs to the field of applications in agriculture and food science [ 8 , 9 , 12 , 13 , 14 , 21 , 22 ], a field from which aquaphotomics originated, and probably the reason why this category is still at the forefront of aquaphotomics development. Following this category are the preprocessing and chemometrics [ 2 , 4 , 15 , 18 ] and fundamental studies [ 5 , 6 , 10 , 17 ]. The former category reflects an important issue that aquaphotomics is trying to resolve, namely, the necessity of developing appropriate tools for dealing with the spectra of highly aqueous samples, and that significant efforts are being invested in resolving the issues of extracting the information from very subtle spectral signals.…”

“…That is why the beginning of the book presents papers dealing with the topics of preprocessing, chemometrics, and the related tools that should lead to improved ways of extracting the relevant information from the spectra. In the first review of this Issue, Roger, Mallet, and Marini [ 2 ] provided a comprehensive overview of the most useful preprocessing and chemometrics techniques that are particularly relevant for the nature of signals aquaphotomics is dealing with. If one should choose just one, the most important message of this review should be as follows: “It is important to look at the “raw” spectra before deciding which preprocessing to use!”.…”