Fast quantitative detection of black pepper and cumin adulterations by near-infrared spectroscopy and multivariate modeling

Lima, Amanda Beatriz Sales de; Batista, Acsa Santos; Jesus, Josane Cardim de; Silva, Jaqueline de Jesus; Araújo, Antônia Cardoso Mendes de; Santos, Leandro Soares

doi:10.1016/j.foodcont.2019.106802

Cited by 70 publications

(37 citation statements)

References 33 publications

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“…During collecting, the temperature was kept at about 25°C in the laboratory. The Unity InfoStar V3.11.3 software was used for spectrometer configuration, control, and data acquisition (Lima et al, 2020;Santos et al, 2021). The spectral regions and the main functional groups of the samples analyzed in the NIRS were identified in the literature (Sunoj et al, 2016).…”

Section: Near-infrared Spectroscopy (Nirs)mentioning

confidence: 99%

“…The number of latent variables and the performance of the obtained PLS models were evaluated considering the correlation coefficient (R), the Root-Mean-Square Error (RMSE), the ratio of performance to deviation (RPD) and the range error ratio (RER) (Lima et al, 2020). The analysis was performed using Chemoface software version 1.63.…”

Section: Partial Least Squares (Pls)mentioning

confidence: 99%

“…Fourier transform near-infrared spectroscopy (FT-NIR), associated with chemometrics, may be a suitable strategy as it is considered to be fast, sensitive, non-invasive, nondestructive, relatively low cost, providing a large amount of information with only one test, as it requires little or no sample pre-treatment. It is clean, since it eliminates the use of chemical reagents and avoids the generation of waste that is harmful to the environment, and the equipment is relatively easy to handle (Lima et al, 2020). Infrared spectroscopy and chemometrics are techniques that are always allied, because the spectra have wide and overlapping bands and multivariate analysis becomes a valuable tool to remove the information contained therein, allowing the identification and quantification of various parameters in different matrices (Souza, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling

Batista¹,

Oliveira²,

Pereira

et al. 2021

RSD

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Cocoa is a commodity responsible for the income of millions of people and the manufacture of several important products for the food, pharmaceutical, and cosmetic industries. Its quality is associated with several factors involved in the processing steps, mainly in fermentation and drying. The objective of this study was to evaluate the application of near-infrared spectroscopic data associated with multivariate analysis to classify cocoa beans according to their quality and predict attributes such as pH and total acidity by PLS-DA and PLS, respectively. The pH values (4.4-6.7) and total acidity (6.12-29.9) were determined by conventional methods. The PLS-DA proved to be effective in differentiating the classes of cocoa samples with superior and inferior quality, presenting in the validation 100% and 71.43% correct cocoa bean classification with inferior Quality and Higher Quality, respectively. The models obtained by PLS presented satisfactory parameters, being classified as having moderate practical utility and excellent predictive capacity for pH and moderate practical utility and reasonable predictive capacity for total acidity. Thus, the potential of the NIRS technology associated with chemometrics was found and showed efficiency in the classification and prediction of attributes in cocoa beans.

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Section: Near-infrared Spectroscopy (Nirs)mentioning

confidence: 99%

Section: Partial Least Squares (Pls)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling

Batista¹,

Oliveira²,

Pereira

et al. 2021

RSD

Self Cite

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“…Thus, various aspects of quality control of this product are vital to the sustainability of its market 30 – 33 . Prominent examples are the development of analytical methods 34 – 36 to detect adulteration by cheaper materials, e.g., pepper husk or papaya seed, and to uncover geographical origins of peppers 37 . The latter study is directly linked to geographical indication (GI), which has become an increasingly important leveraging tool in trade negotiations 38 , 39 and sales valuation of products.…”

Section: Introductionmentioning

confidence: 99%

Rapid geographical indication of peppercorn seeds using corona discharge mass spectrometry

Charoensumran

Rauytanapanit

Sricharoen

et al. 2021

Sci Rep

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With increasing demands for more rapid and practical analyses, various techniques of ambient ionization mass spectrometry have gained significant interest due to the speed of analysis and abundance of information provided. Herein, an ambient ionization technique that utilizes corona discharge was applied, for the first time, to analyze and categorize whole seeds of black and white peppers from different origins. This setup requires no solvent application nor gas flow, thus resulting in a very simple and rapid analysis that can be applied directly to the sample without any prior workup or preparation. Combined with robust data pre-processing and subsequent chemometric analyses, this analytical method was capable of indicating the geographical origin of each pepper source with up to 98% accuracies in all sub-studies. The simplicity and speed of this approach open up the exciting opportunity for onsite analysis without the need for a highly trained operator. Furthermore, this methodology can be applied to a variety of spices and herbs, whose geographical indication or similar intellectual properties are economically important, hence it is capable of creating tremendous impact in the food and agricultural industries.

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“…Food fraud and security are closely related to human life and play an important role in human survival and development (Lima et al ., 2020). The issues cause significant economic losses for the industry and generate distrust from the consumers and traders (Orrillo et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

Ren

Liu

Ning

et al. 2020

Int J of Food Sci Tech

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Summary Food fraud causes significant economic losses for the industry and generates distrust between the consumers and traders. Tea is one of the most valued beverages throughout the world, being vulnerable to economically motivated cheat. The objective of the study was to develop the potential of hyperspectral imaging (HSI) allying multivariate analysis and data fusion to identify the authenticity of Keemun black tea quality categories. Data fusion that integrated of texture characteristics based on grey level co‐occurrence matrix and visible and near‐infrared spectral features via competitive adaptive reweighted sampling (CARS) was as the target data for modelling. Support vector machine (SVM) and random forest (RF) were utilised for the classification of tea samples of seven grades. The RF model using fused data gave the best performance with the correct discriminant rate of 92.70% for the prediction set. This study demonstrated that HSI coupled with RF was effective in identifying tea sample rank.

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Fast quantitative detection of black pepper and cumin adulterations by near-infrared spectroscopy and multivariate modeling

Cited by 70 publications

References 33 publications

Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling

Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling

Rapid geographical indication of peppercorn seeds using corona discharge mass spectrometry

Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion

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