Evaluation of green coffee beans quality using near infrared spectroscopy: A quantitative approach

Santos, João Rodrigo; Sarraguça, Mafalda C.; Rangel, António O.S.S.; Lopes, João A.

doi:10.1016/j.foodchem.2012.06.059

Cited by 69 publications

(37 citation statements)

References 21 publications

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“…Many other applications that have been reported in the literature include, for example, the rapid detection of citric and tartaric acids in orange juice , the evaluation of green coffee beans (Santos et al 2012), and the analysis of changes in the secondary structure of gluten proteins due to emulsifiers (Gómez et al 2013). Product moisture content is important in most foods.…”

Section: Infrared Spectroscopymentioning

confidence: 99%

Control and Monitoring of Food-Manufacturing Processes

McFarlane

2013

Handbook of Food Factory Design

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This chapter begins with descriptions of the building blocks of control systems, which include the sensors for process parameters and product attributes, as well as the control modules, operator facilities and reporting devices. In the later parts of the chapter, methods for the incorporation of these building blocks in control schemes and information systems are reviewed, with special reference to system reliability and database management. In the final section, there is a guide for matching instrumentation and process equipment in specific forms of processing. InstrumentationSensors may be categorized as follows:• General purpose sensors for process conditions • Sensors for humidity and moisture • Chemical composition sensors • Devices for measurement of size, shape, and color • Devices for sorting and sensing of foreign bodies.The first category differs from the others because sensing of process conditions is a common requirement in all sectors of manufacturing industry; food manufacturers can therefore take advantage of the economy of scale and degree of reliability associated with long and widespread use.In all the other categories, not only are there the costs of developing or adapting sensors for particular applications, but there are also the delays and uncertainty associated with such developments. A lack of suitable in-line sensors was highlighted as far back as the mid-1980s (Wren 1985. Brimelow (2001), Tothill (2003), Patel and Beveridge (2003) and Zhou and Therdthai (2012) provide comprehensive descriptions of current instrumentation and sensor technology. Despite the developments that have occurred in recent years, there is still much to be done. Measurement of food quality parameters in real time still largely remains an elusive goal.

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Section: Infrared Spectroscopymentioning

confidence: 99%

Control and Monitoring of Food-Manufacturing Processes

McFarlane

2013

Handbook of Food Factory Design

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“…For coffee, NIR spectroscopy has been applied for evaluation of geographic and genotypic origin of arabica coffee [1]. 3 To whom any correspondence should be addressed. NIR spectroscopy also is often used to discriminate blends of arabica and robusta grains, obtaining satisfactory results [2], [3], [4] or to detect defects or adulteration in coffees [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…3 To whom any correspondence should be addressed. NIR spectroscopy also is often used to discriminate blends of arabica and robusta grains, obtaining satisfactory results [2], [3], [4] or to detect defects or adulteration in coffees [5], [6]. However, no studies reported NIR spectroscopy for discrimination of civet and non-civet coffee.…”

Section: Introductionmentioning

confidence: 99%

The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS₂regression method

Yulia

Suhandy

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. NIR spectra obtained from spectral data acquisition system contains both chemical information of samples as well as physical information of the samples, such as particle size and bulk density. Several methods have been established for developing calibration models that can compensate for sample physical information variations. One common approach is to include physical information variation in the calibration model both explicitly and implicitly. The objective of this study was to evaluate the feasibility of using explicit method to compensate the influence of different particle size of coffee powder in NIR calibration model performance. A number of 220 coffee powder samples with two different types of coffee (civet and noncivet) and two different particle sizes (212 and 500 µm) were prepared. Spectral data was acquired using NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement. A discrimination method based on PLS-DA was conducted and the influence of different particle size on the performance of PLS-DA was investigated. In explicit method, we add directly the particle size as predicted variable results in an X block containing only the NIR spectra and a Y block containing the particle size and type of coffee. The explicit inclusion of the particle size into the calibration model is expected to improve the accuracy of type of coffee determination. The result shows that using explicit method the quality of the developed calibration model for type of coffee determination is a little bit superior with coefficient of determination (R 2 ) = 0.99 and root mean square error of cross-validation (RMSECV) = 0.041. The performance of the PLS2 calibration model for type of coffee determination with particle size compensation was quite good and able to predict the type of coffee in two different particle sizes with relatively high R 2 pred values. The prediction also resulted in low bias and RMSEP values.

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“…Classical point-wise NIR spectroscopy has been widely used to discriminate Arabica and Robusta species, both on green coffee [35,36] and roasted coffee [37,38]. Moreover some research works have been also reported where hyperspectral imaging is used to characterize these coffee species [39,40].…”

Section: Introductionmentioning

confidence: 99%

Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

Calvini

Ulrici

Amigo

2015

Chemometrics and Intelligent Laboratory Systems

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In the present work sparse-based methods are applied to the analysis of hyperspectral images with the aim at studying their capability of being adequate methods for variable selection in a classification framework. The key aspect of sparse methods is the possibility of performing variable selection by forcing the model coefficients related to irrelevant variables to zero. In particular, two different sparse classification approaches, i.e. sPCA+kNN and sPLS-DA, were compared with the corresponding classical methods (PCA + kNN and PLS-DA) to classify Arabica and Robusta coffee species. Green coffee samples were analyzed using near infrared hyperspectral imaging and the average spectra from each hyperspectral image were used to build training and test sets; furthermore a test image was used to evaluate the performances of the considered methods at pixel-level. In our case, sparse methods led to similar results as classical methods, with the advantage of obtaining more interpretable and parsimonious models. An important result to highlight is that variable selection performed with two different sparse classification approaches converged to the selection of same spectral regions, which implies the chemical relevance of those regions in the discrimination of Arabica and Robusta coffee species

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Evaluation of green coffee beans quality using near infrared spectroscopy: A quantitative approach

Cited by 69 publications

References 21 publications

Control and Monitoring of Food-Manufacturing Processes

Control and Monitoring of Food-Manufacturing Processes

The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS₂regression method

Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

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Evaluation of green coffee beans quality using near infrared spectroscopy: A quantitative approach

Cited by 69 publications

References 21 publications

Control and Monitoring of Food-Manufacturing Processes

Control and Monitoring of Food-Manufacturing Processes

The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS2regression method

Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

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The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS₂regression method