Rapid determination of sucrose in chocolate mass using near infrared spectroscopy

Filho, Paulo Augusto da Costa

doi:10.1016/j.aca.2008.10.049

Cited by 43 publications

(21 citation statements)

References 24 publications

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“…It is capable of calibrating mixtures with almost identical spectra without loss of prediction capacity using the spectrophotometric method. Many studies have demonstrated the importance of the validation step in GA wavelength selection for multiple linear regression in order to avoid random correlation and the selection of irrelevant variables [43,46,77,[79][80][81].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Variables selection methods in near-infrared spectroscopy

Zhang

Zhao

Povey

et al. 2010

Analytica Chimica Acta

861

363

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Section: Genetic Algorithmmentioning

confidence: 99%

Variables selection methods in near-infrared spectroscopy

Zhang

Zhao

Povey

et al. 2010

Analytica Chimica Acta

861

363

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“…Recent studies on food products, such as assessment of beer quality using computer vision algorithms, NIRS and machine learning algorithms [11], prediction of pH and total soluble solids in banana using NIRS [12], prediction of canned black bean texture using visible/NIRS [13], and discriminant analysis of pine nuts by NIRS [14] have been conducted. Applications of NIRS to cocoa and chocolate manufacture include quality control of cocoa beans [8], determination of biochemical quality parameters in cocoa [15], rapid determination of sucrose content in chocolate mass [16], and assessment of raw cocoa beans to predict the sensory properties of chocolate [17].…”

Section: Introductionmentioning

confidence: 99%

Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling

Gunaratne

Viejo

Gunaratne

et al. 2019

Foods

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Chocolates are the most common confectionery and most popular dessert and snack across the globe. The quality of chocolate plays a major role in sensory evaluation. In this study, a rapid and non-destructive method was developed to predict the quality of chocolate based on physicochemical data, and sensory properties, using the five basic tastes. Data for physicochemical analysis (pH, Brix, viscosity, and color), and sensory properties (basic taste intensities) of chocolate were recorded. These data and results obtained from near-infrared spectroscopy were used to develop two machine learning models to predict the physicochemical parameters (Model 1) and sensory descriptors (Model 2) of chocolate. The results show that the models developed had high accuracy, with R = 0.99 for Model 1 and R = 0.93 for Model 2. The thus-developed models can be used as an alternative to consumer panels to determine the sensory properties of chocolate more accurately with lower cost using the chemical parameters.

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“…NIR spectroscopy was reported for achieving excellent predictions of sucrose in chocolate, with a correlation coefficient of 0.997 or higher (Da Costa Filho, 2009). Vis/NIR spectroscopy was also reported for measuring the sucrose content of sugarcane.…”

Section: Introductionmentioning

confidence: 95%