Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses

Abstract

Effective and fast methods are important for distinguishing cocoa varieties in the field and in the processing industry. This work proposes the application of NIR spectroscopy as a potential analytical method to classify different varieties and predict the chemical composition of cocoa. Chemical composition and colour features were determined by traditional methods and then related with the spectral information by partial least-squares regression. Several mathematical pre-processing methods including first and second derivatives, standard normal variate and multiplicative scatter correction were applied to study the influence of spectral variations. The results of chemical composition analysis and colourimetric measurements show significant differences between varieties. NIR spectra of samples exhibited characteristic profiles for each variety and principal component analysis showed different varieties in according to spectral features.

Relationship between bioactive compounds and sensory properties of dark chocolate produced from Brazilian hybrid cocoa

“…Barbin et al . (2018) also studied the PH16, BN34, SR162, and CEPEC2002 cocoa varieties by traditional methods in relation to their chemical composition and colour features. The authors reported that the results of the colorimetric measurements showed significant differences among the varieties.…”

Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near‐infrared spectroscopy and multivariate analysis to identify affected chicken breasts

“…There is no standard spectral preprocessing method to apply in a given dataset. Thus, it is often adopted a trial and error approach, observing which method gives the best results (Barbin et al., 2018). The raw data were visually inspected and some data preprocessing strategies, such as Savitzky–Golay smoothing (second‐order polynomial, 21 window points), 1st derivative and 2nd derivative, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC) were evaluated in terms of overall classification errors.…”

Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods

“…These physical conditions can cause variations in measured spectra, and have been identified in spectra as multiplicative and additive effects. These effects, due to light scatter, are minimized using a sample of a small homogenized particle size (Barbin et al., ). Most studies have employed ground beans more than whole beans, partly as a way to minimize the aforementioned variations and effects (Barbin et al., ) (Table )…”

“…These effects, due to light scatter, are minimized using a sample of a small homogenized particle size (Barbin et al, 2018). Most studies have employed ground beans more than whole beans, partly as a way to minimize the aforementioned variations and effects (Barbin et al, 2018) (Table 3) In relation to measurement modes, Dickens and Dickens (1999) defined four ways to implement measurement equipment into processes: (a) offline: a sample analysis run away from the production line (i.e., laboratory); (b) at line: manual random sample extraction from the production line and an analysis performed close to the process line; (c) online: samples separated from the production line which, after being analyzed in a recirculation loop (bypass), are returned; (d) inline: samples are analyzed on the running production line (in situ; Dickens & Dickens, 1999;Osborne, 2000). Table 3 shows the performance of this nondestructive analysis done in the offline mode in almost all the studies carried out by NIR in cocoa beans.…”