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Article

Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy

by
Woranitta Sahachairungrueng
1,
Chanyanuch Meechan
2,
Nutchaya Veerachat
2,
Anthony Keith Thompson
3 and
Sontisuk Teerachaichayut
2,*
1
Department of Food Science, School of Food-Industry, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand
2
Department of Food Process Engineering, School of Food-Industry, King Mongkut’s Institute of Technology Ladkrabang, Chalongkrung Road, Bangkok 10520, Thailand
3
Department of Postharvest Technology, Cranfield University, College Road, Bedford MK43 0AL, UK
*
Author to whom correspondence should be addressed.
Foods 2022, 11(19), 3122; https://doi.org/10.3390/foods11193122
Submission received: 2 September 2022 / Revised: 30 September 2022 / Accepted: 3 October 2022 / Published: 7 October 2022
(This article belongs to the Special Issue Analytical Methods in Detecting Food Fraud and Food Authenticity)
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Abstract

:
It has been reported that some brands of roasted ground coffee, whose ingredients are labeled as 100% Arabica coffee, may also contain the cheaper Robusta coffee. Thus, the objective of this research was to test whether near-infrared spectroscopy hyperspectral imaging (NIR-HSI) or Fourier transform infrared spectroscopy (FTIRs) could be used to test whether samples of coffee were pure Arabica or whether they contained Robusta, and if so, what were the levels of Robusta they contained. Qualitative models of both the NIR-HSI and FTIRs techniques were established with support vector machine classification (SVMC). Results showed that the highest levels of accuracy in the prediction set were 98.04 and 97.06%, respectively. Quantitative models of both techniques for predicting the concentration of Robusta in the samples of Arabica with Robusta were established using support vector machine regression (SVMR), which gave the highest levels of accuracy in the prediction set with a coefficient of determination for prediction (Rp2) of 0.964 and 0.956 and root mean square error of prediction (RMSEP) of 5.47 and 6.07%, respectively. It was therefore concluded that the results showed that both techniques (NIR-HSI and FTIRs) have the potential for use in the inspection of roasted ground coffee to classify and determine the respective levels of Arabica and Robusta within the mixture.

1. Introduction

Several factors can influence the flavor of brewed coffee, including: the climate, environment and soil where the plants are grown, as well as the harvesting method and maturity of the beans, but a major influence is the species from which the raw beans are obtained [1,2]. There are more than 70 species of Coffea, with Arabica (C. arabica), which originated in the Ethiopian highland, and Robusta (C. canephora), which originated in central and western sub-Saharan Africa, being the main cultivated species. Arabica accounts for approximately 60 to 70% of the total cultivated worldwide. The coffee made from these two species has different characteristics and beans can be identified visually, but it is not possible to differentiate between them visually when they have been ground. The price of Arabica beans is approximately twice that of Robusta beans; therefore, branded ground coffee or instant coffee can be a target of fraud, often through the partial or complete substitution of Arabica with Robusta [3]. Reference [4] tested 22 roasted ground coffee samples, labeled 100% Arabica coffee, from 12 different brands from shopping malls and coffee stores in Taiwan and found that four samples were adulterated with Robusta coffee. Moreover, ground coffee has been the target of fraudulent admixtures with cheaper materials, including coffee husks and other roasted grains [5] and roasted barley [6]. Many commercial brands of ground coffee contain blends of Arabica and Robusta, which are blended in order to achieve a flavor typical of that particular brand at a price that optimizes the market for that brand. Arabica has half the caffeine of Robusta, but it is a delicate plant that requires more attention and care and is sensitive to pests and diseases. It is also cultivated in higher altitude land (in Colombia coffee for export can only be grown by law between 1200 and 1800 m above sea level). Robusta is grown at heights between 200 and 800 m above sea level, does not require as much care as Arabica, is more resistant to pests and diseases and has a higher yield per plant.
The NIR hyperspectral imaging (NIR-HSI) technique combines spectroscopy and computer vision techniques in order to provide both spectral and spatial data that associates with the characterization, composition and properties of the samples [7]. The hyperspectral imaging technique can be applied as a fast, non-destructive method to detect contamination of powdered food [7,8]. Previously, research has reported on using NIR-HSI for evaluating the adulteration of powdered food such as prediction of black pepper adulteration [9,10], adulterations in wheat products with cheap grains [11], identification and quantification of adulterants in milk powder [12,13,14], detection of adulteration of peanut flour in chocolate powder [8] and prediction of concentration of adulterants in tapioca starch [15].
Fourier transform infrared (FTIR) spectroscopy is one type of infrared spectrometry that uses the principle of absorption energy of molecules based on their vibrations. Different molecules have different spectra [16]. This method acquires the spectral information of samples in the infrared wavelength range and measurement is fast, non-destructive, accurate, reliable [16] and does not use any chemicals [17]. FTIR spectroscopy can be used for both qualitative and quantitative analyses [18] and has been used both to identify chemical components in samples and to detect contaminants in samples [19]. In addition, it can be used for the analysis of adulteration in food [17]. FTIR spectroscopy was successfully used for detecting adulteration in foods such as prediction of adulteration of starch in onion powder [18], adulteration both qualitative and quantitative in tea [20,21], adulteration in paprika powder [22] and adulteration in garlic powder [23,24]. In summary, both NIR-HSI and FTIR spectroscopy techniques have been successfully used for detecting adulteration in foods. Therefore, the aim of this research is to compare the performance of both techniques for evaluating the adulteration in Arabica ground coffee with Robusta ground coffee based on classification and calibration models. This will be useful for the industries in order to consider a suitable technique to inspect the raw material of the ground roasted coffee before feeding it to the processor.

2. Materials and Methods

2.1. Sample Preparation

The Arabica roasted coffee beans were purchased from a coffee plantation in Chiang Rai province in northern Thailand and the Robusta roasted coffee beans from a coffee plantation in Chumphon province in southern Thailand and then ground in a blender (Blender 480, Kenwood, Thailand). Samples of Arabica were mixed with samples of Robusta at a ratio of 1% to 99% (w/w) and increasing every 1% (w/w) until the ratio was 99% to 1% (w/w). Each of the samples was stored separately in a zip-lock plastic bag at 25 °C until required for NIR-HSI and FTIRs measurements.

2.2. NIR-HSI and FTIRs Data Acquisition

Each sample in a container was scanned using NIR hyperspectral imaging (Specim FX17e, Spectral Imaging Ltd., Oulu, Finland) in an air conditioned room at 25 °C (Figure 1A). Scanning was performed by 224 spectral bands in the wavelength range of 935–1720 nm with an interval of 3.5 nm in the reflectance mode with a scanning speed of 15 mm/s. Two references were used: a black reference (Rb) by closing the shutter and covering it with a lid on the camera lens, and a white reference (Rw) using a rectangular Spectralon bar.
Each sample was placed in an attenuated total reflectance (ATR) crystal and then scanned using an FTIR spectrophotometer (Bruker Corporation, INVENIO-S, Ettlingen, Germany) with a DLaTGS detector in the air conditioning room at 25 °C (Figure 1B). The absorbance spectra were collected using 64 scans in the wavenumbers of 4000 cm−1 to 400 cm−1 with the interval of 4 cm−1 in the reflection mode and the average spectrum of each sample, contained with 2519 independent variables, was acquired.

2.3. Principal Component Analysis (PCA)

PCA was used for verifying the possibility of discrimination between two groups based on spectral information of both NIR-HSI and FTIRs. Additionally, PCA was carried out for comparison between samples of pure Arabica and pure Robusta as well as between samples of pure Arabica and Arabica with Robusta.

2.4. Support Vector Machine (SVM)

Samples from each of the measurements were divided between two sets. One set was used for establishing the models, while the other was used for testing the models. For qualitative analysis, the dependent variables were 0 (pure Arabica) and 1 (Arabica diluted with Robusta), and the independent variables were spectral data. For quantitative analysis, the dependent variables were the concentration levels of Robusta in samples, and the independent variables were spectral data. Before creating the models, the acquired original spectra of samples in the calibration set were preprocessed using spectral pretreatment methods (smoothing, first derivative, second derivative, MSC, SNV) and combined methods in order to obtain the optimum models for predicting both qualitative and quantitative analyses.
Previously, methods have been successfully used to establish the model from spectral data, including partial least squares regression (PLSR) [18,25], multiple linear regression (MLR) [26], principal component regression (PCR) [27] and support vector machine (SVM) [28]. Normally, PLSR is used for analysis since it has been shown to give better results when compared to MLR and PCR [29,30]. However, some research reports indicate that when SVM and PLSR were compared for qualitative and quantitative analysis [31] the adulteration of melamine in liquid milk was detected and they found the SVM model showed better results than the PLSR model. Ref. [32] used the SVM model for determining authentication of Spanish protected designation of origin (PDO) wine vinegars that the SVM model provided a better result in discrimination than those of the PLS-DA model. Ref. [33] tried to use the SVM and PLSR model for predicting the physicochemical properties of soybean paste. The results showed the SVM models gave more precisely than those of the PLSR models. Thus, SVM was selected for establishing the models for comparison of the two techniques in this study.
SVM is a machine learning technique that is used for classification and regression analysis depending on statistical principles [34]. SVM models are developed based on calculating a distance metric among data vectors by the concept of the structural risk minimization induction principle therefore it can obtain a good solution in high dimensional spaces that helps to increase the efficiency of the model [35,36]. It can be applied to establish models for both SVMC and SVMR [34].
For qualitative analysis, SVMC is a supervised method of classification that uses separate categories for two classes [37]. Qualitative models were created for classifying the dependent variable that the number of 0 was a representative of a class of pure Arabica while the number of 1 was a representative of a class of adulterated Arabica with Robusta. The independent variables were spectral data at various wavelengths and wave numbers for NIR-HSI and FTIRs, respectively. SVMC was used for developing the classification models for discriminating between pure Arabica and adulterated Arabica with Robusta in this study. Spectral data in the calibration set were preprocessed using various spectral pretreatment methods. The best models were selected by cross-validation. To evaluate the predictive ability of the models, the models were tested by the samples in the prediction set. The acquired predicted values from the models were determined by comparing them with the actual values. The result of each sample was presented as positive true, negative true, positive false or negative false. The classification models were evaluated for accuracy by Equation (1), specificity by Equation (2), sensitivity by Equation (3) and error rate by Equation (4). Accuracy is used to present the overall accuracy of the classification model. Both specificity and sensitivity are used together to present the predictive performance of the classification model. Finally, the error rate is used to present to misclassify of overall prediction of the classification model [38,39]. The same procedures in
Accuracy   ( % ) = ( T P + T N ) T o t a l × 100
Specificity   ( % ) = T N ( T N + F P ) × 100
Sensitivity   ( % ) = T P ( T P + F N ) × 100
Error   rate   ( % ) = ( F P + F N ) T o t a l × 100
where: TP = true positive, TN = true negative, FP = false positive, and FN = false negative.
For quantitative analysis, SVMR is a regression analysis of SVM which is used to establish the calibration model that correlated between the spectra data and the dependent variables [40]. Quantitative models were created for predicting the dependent variable, which was the concentration of Robusta in the samples. The independent variables were spectral data at various wavelengths and wave numbers for NIR-HSI and FTIRs, respectively. SVMR was used for developing the models for predicting the concentration of Robusta in this study. Spectral data in the calibration set were preprocessed by various spectral pretreatment methods. The best models were selected by considering the results of cross-validation using the coefficient of determination (Rcv2) and root mean square error (RMSECV). To evaluate the predictive ability of the models, the models were tested by the samples in the prediction set. The accuracies of the models were considered by the coefficient of determination for prediction (Rp2) and root mean square error for prediction (RMSEP).
The Prediktera Evince software version 2.7.9 and the OPUS program version 8.5.29 was used for device control and data acquisition for NIR-HSI and FTIRs, respectively. Statistical results were analyzed by the Unscrambler X 10.4.

3. Results and Discussion

3.1. Average Spectra Obtained from the NIR-HSI and FTIRs

The spectral image (640 × 1185 pixels) in the wavelength range of 935–1720 nm was obtained from scanning with the NIR-HSI technique. Each spectral image of the samples was removed from the spectra of the background and the edge of the container, and then the spectra of each sample were averaged. The average spectrum of each sample in the full wavelength range was used for both qualitative and quantitative analysis. Additionally, the acquired spectra of each sample from FTIRs in the wavenumbers of 4000 cm−1 to 400 cm−1 were averaged and then the average spectrum of each sample in the full wave numbers was also used for both qualitative and quantitative analysis.

3.2. Characterization of Spectra for Ground Roasted Coffee Using NIR-HSI

The average original absorbance spectra and 2nd derivative absorbance spectra of pure Arabica and pure Robusta by NIR-HSI in the wavelength of 935–1720 nm (Figure 2), Show only a few peaks in the original absorbance spectra. The absorbance peaks were observed at around 1200 and 1450 nm, which are related to the overtone vibrations of the second overtones and the first overtones of OH stretching, respectively, as shown in Figure 2A and previously reported by [41,42]. The original absorbance spectra were preprocessed with the 2nd derivative technique, which reduced overlaps of peaks in the spectra. The 2nd derivative spectra were used to identify the main components of both Arabica and Robusta. The feature of both spectra of pure Arabica and pure Robusta showed peaks at the same wavelength. Those peaks of the 2nd derivative spectra present chemical components in the ground roasted coffee as reported by [43]. The spectra showed peaks of components in coffee that were not only water but also chlorogenic acid, caffeine, trigonelline and carbohydrates. The acquired absorbance peaks show that around 1412–1444 nm is the first overtone of O-H and N-H, which is related to chlorogenic acid and caffeine as shown in Figure 2B [44]. There were peaks of chlorogenic acid at 1100 and 1500 nm which are the second overtone of C-H stretching and the first overtone of C-H stretching [45,46]. The acquired spectra showed a clear peak of caffeine at 1216 nm which is related to molecules’ bonding with the second overtone of C-H stretching [47]. The acquired spectra also showed the peak at around 1366–1378 nm that is correlated to the second overtone of C-H, which is trigonelline, as well as the peak at around 1626–1640 nm concerns to the first overtone of C-H related to trigonelline and caffeine [48]. The peak of carbohydrates showed at around 1584 nm, which has been shown to be associated with the first overtone of the O-H stretch [45].

3.3. Characterization of Spectra for Ground Roasted Coffee by FTIRs

Average spectra of pure Arabica and pure Robusta by FTIRs show the peaks of components in ground roasted coffee (Figure 3) where both spectra of pure Arabica and pure Robusta showed peaks at the same wave numbers indicating that the spectra showed the peaks of components in coffee including water, caffeine, lipid, chlorogenic acid and carbohydrates. Water peaks showed at the wavenumber around 3300 cm−1 in the region of 3676–3028 cm−1 [49,50]. Caffeine peaks showed at the wavenumber around 2852 cm−1 and in the region of 1650–1600 cm−1 [51,52,53]. Lipid peaks were attributed at the wavenumber around 1744 cm−1 and in the region of 2908–2920 cm−1 which is related to stretching vibration of the carbonyl (C=O) and stretching asymmetric C-H of CH2 groups [53,54]. The peaks of chlorogenic acid, which occurs by esterification between quinic acid and caffeic acid, showed in the range of 1450–1000 cm−1 [5]. The peaks of carbohydrates showed in the region of 1500–700 cm−1 [5].

3.4. PCA

The acquired spectral data of samples from NIR-HSI and FTIRs were used for the principal component analysis in order to determine the potential of classification of pure Arabica and pure Robusta, as well as the Arabica diluted with Robusta. For pure Arabica and pure Robusta, the score plot of PC1 and PC2 by PCA based on spectral data from NIR-HSI (Figure 4A) shows complete separation between groups of pure Arabica and pure Robusta with the variance percentage of 97% and 3% for PC1 and PC2, respectively. While the score plot of PC1 and PC2 by PCA is based on spectral data from FTIRs (Figure 5A). shows overlapping between groups of pure Arabica and pure Robusta with the variation of 95% and 2% for PC1 and PC2, respectively. For pure Arabica and the Arabica with Robusta, the score plot of PC1 and PC2 by PCA based on spectral data from NIR-HSI (Figure 4B) almost separates between groups of Arabica and Arabica with Robusta with the variance percentage of 98% and 1% for PC1 and PC2, respectively. While the score plot of PC1 and PC2 by PCA based on spectral data from (Figure 5B) shows overlapping between groups of Arabica and Arabica with Robusta with the variation of 50% and 11% for PC1 and PC2, respectively. Therefore, the classification results by using PC1 and PC2 showed that spectral data from NIR-HSI had a better potential when compared with FTIRs.
Samples from measurements were divided into the calibration set and the prediction set for qualitative and quantitative analysis (Table 1). Acquired full-spectrum data of NIR-HSI and FTIRs was used in order to establish the models using SVM. For the qualitative study, the classification between samples of pure Arabica (defined as 0) and the Arabica with Robusta (defined as 1) was analyzed. The pure Arabica samples were divided into the same ratio for both sets and the deviation of the concentration of Robusta in both sets was similar. The samples in the calibration set were 206 and the samples in the prediction set were 102 for both NIR-HSI and FTIRs. For the quantitative study, dependent variables were the various concentration levels of Robusta in the samples of the Arabica with Robusta. The deviation of the concentration of Robusta of samples in the calibration set and the prediction set was similar. The samples in the calibration set were 136 and the samples in the prediction set were 66 for both NIR-HSI and FTIRs.

3.5. Qualitative Analysis

Due to the effects on spectra during measurement, including noise, scatter and baseline shift, the data obtained from the NIR-HSI and FTIRs were improved by carrying out the pre-treatment methods of smoothing, first derivative, second derivative, MSC, SNV and combined methods in order to reduce those effects and produce the most accurate model.
For qualitative analysis, Figure 6 shows the confusion matrix for classification using SVMC models. Comparison between results of NIR-HSI and FTIRs was investigated resulting in NIR-HSI giving the best result of classification, obtained by preprocessing the spectra before creating the model with SNV, which correctly predict 98.48% (130/132) for class 1 and 100% (74/74) for class 0 in the calibration set and 96.97% (64/66) for class 1 and 100% (36/36) for class 0 in the prediction set. The overall accuracy for prediction was 98.04% (100/102). Using FTIRs, the best result for classification was obtained by preprocessing the spectra before creating the model with the first derivative combined with SNV. Using this method, it was able to correctly predict 95.45% (126/132) for class 1 and 100% (74/74) for class 0 in the calibration set and 95.45% (63/66) for class 1 and 100% (36/36) for class 0 in the prediction set. The overall accuracy for prediction was 97.06% (99/102) (Table 2).

3.6. Quantitative Analysis

The same procedures described for qualitative analysis were used for quantitative analysis. The results showed that the spectral data obtained from NIR-HSI and FTIRs were improved by using pre-treatment. For quantitative analysis, the calibration models for predicting the concentration of Robusta were established using SVMR, with NIR-HSI giving the best results for the calibration model that had been obtained by preprocessing spectra data with the first derivative combined with SNV pretreatment method with Rp2 and RMSEP of 0.964 and 5.47%, respectively. When using FTIRs, the best result of the calibration model was obtained by preprocessing spectra data with the first derivative combined with MSC pretreatment method with Rp2 and RMSEP of 0.956 and 6.07%, respectively. The summarized results of the calibration models for predicting the concentration of Robusta in the Arabica with Robusta (Table 3) showed that both techniques could be successfully used for prediction but, the NIR-HSI technique had slightly better accuracy than the FTIRs based on higher Rp2 and lower RMSEP.
Figure 7 shows the scatter plots of samples in the calibration set and prediction set that used SVMR models for predicting the concentration of Robusta compared with the actual concentration by using NIR-HSI and FTIRs. The plots of data were close to the 45° line, which indicated good accuracy of the models for prediction.

4. Conclusions

It was shown that for either qualitative and quantitative determinations of the levels of Robusta coffee in samples of Arabica coffee, both near-infrared spectroscopy hyperspectral imaging and Fourier transform infrared spectroscopy had potential. Moreover, this study showed that models using NIR-HSI gave higher predictive accuracy for indicating the differences between pure Arabica and Arabica with Robusta and could also be used to determine the concentration of Robusta in the Arabica with Robusta mixture. It was therefore concluded that the results showed that both techniques (NIR-HSI and FTIRs) have the potential for use in the inspection of roasted ground coffee to determine the respective levels of Arabica and Robusta within a mixture of the two.

Author Contributions

Conceptualization, S.T.; methodology, W.S. and S.T.; validation, S.T.; formal analysis, W.S.; data curation, W.S., C.M. and N.V.; writing—original draft preparation, W.S.; writing—review and editing, A.K.T.; supervision, S.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by King Mongkut’s Institute of Technology Ladkrabang and the School of Food Industry, King Mongkut’s Institute of Technology Ladkrabang, grant number 2563-01-07005.

Data Availability Statement

Not applicable.

Acknowledgments

The authors are grateful Panmanas Sirisomboon for technical help.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Farah, A. Coffee: Production, Quality and Chemistry, 1st ed.; The Royal Society of Chemistry: London, UK, 2019. [Google Scholar]
  2. Clarke, R.J.; Macrae, R. Coffee: Volume 1. Chemistry; Elsevier Applied Science Publishers: London, UK, 1985. [Google Scholar]
  3. Kennedy, S.P.; Gonzales, P.; Roungchun, J. Chapter 8: Coffee and tea fraud. In Food Fraud: A Global Threat with Public Health and Economic Consequences, 1st ed.; Charlotte Cockle: Chennai, India, 2021; pp. 139–150. [Google Scholar]
  4. Tentamus Lab for Life. Taiwanese Market Survey on the Adulteration of Coffee Beans. Available online: https://www.tentamus.com/taiwanese-market-survey-adulteration-coffee-beans/ (accessed on 6 July 2021).
  5. Reis, N.; Franca, A.S.; Oliveira, L.S. Discrimination between roasted coffee, roasted corn and coffee husks by Diffuse Reflectance Infrared Fourier Transform Spectroscopy. LWT-Food Sci. Technol. 2013, 50, 715–722. [Google Scholar] [CrossRef] [Green Version]
  6. Oliveira, R.C.S.; Oliveira, L.S.; Franca, A.S.; Augusti, R. Evaluation of the potential of SPME-GC-MS and chemometrics to detect adulteration of ground roasted coffee with roasted barley. J. Food Compos. Anal. 2009, 22, 257–261. [Google Scholar] [CrossRef]
  7. Velásqueza, L.; Cruz-Tiradoa, J.P.; Sichea, R.; Quevedob, R. An application based on the decision tree to classify the marbling of beef by hyperspectral imaging. Meat Sci. 2017, 133, 43–50. [Google Scholar] [CrossRef] [PubMed]
  8. Laborde, A.; Puig-Castellvi, F.; Bouveresse, D.; Eveleigh, L.; Cordella, C.; Jaillais, B. Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and Multivariate Curve Resolution. Food Control 2021, 119, 107454. [Google Scholar] [CrossRef]
  9. McGoverin, C.; September, D.; Geladi, P.; Manley, M. Near infrared and mid-infrared spectroscopy for the quantification of adulterants in ground black pepper. J. Near Infrared Spectrosc. 2012, 20, 521–528. [Google Scholar] [CrossRef]
  10. Orrilloa, I.; Cruz-Tiradob, J.P.; Cardenasa, A.; Orunaa, M.; Carneroa, A.; Barbinb, D.F.; Sichea, R. Hyperspectral imaging as a powerful tool for identification of papaya seeds T in black pepper. Food Control 2019, 101, 45–52. [Google Scholar] [CrossRef]
  11. Verdu, S.; Vasquez, F.; Grau, R.; Ivorra, E.; Sanchez, A.J.; Barat, J.M. Detection of adulterations with different grains in wheat products based on the hyperspectral image technique: The specific cases of flour and bread. Food Control 2016, 62, 373–380. [Google Scholar] [CrossRef]
  12. Fu, X.; Kim, M.S.; Qin, K.C.; Lim, J.; Lee, H.; Garrido-Varo, A.; Perez-Marin, D.; Ying, Y. Detection of melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses. J. Food Eng. 2014, 124, 97–104. [Google Scholar] [CrossRef]
  13. Huang, M.; Kim, M.S.; Delwich, S.R.; Chao, K.; Qin, J.; Mo, C.; Esquerre, C.; Zhu, Q. Quantitative analysis of melamine in milk powders using near-infrared hyperspectral imaging and band ratio. J. Food Eng. 2016, 181, 10–19. [Google Scholar] [CrossRef] [Green Version]
  14. Forchetti, D.A.P.; Poppi, R.J. Use of NIR hyperspectral imaging and multivariate curve resolution (MCR) for detection and quantification of adulterants in milk powder. LWT-Food Sci. Technol. 2017, 76, 337–343. [Google Scholar] [CrossRef]
  15. Khamsopha, D.; Sahachairungrueng, W.; Teerachaichayut, S. Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch. Food Control 2021, 123, 107781. [Google Scholar] [CrossRef]
  16. Subramanian, A.; Rodriguez-Saona, L. Chapter 7—Fourier Transform Infrared (FTIR) Spectroscopy. In Infrared Spectroscopy for Food Quality Analysis and Control; Sun, D.W., Ed.; Elsevier’s Science & Technology: Oxford, UK, 2021; pp. 146–178. [Google Scholar] [CrossRef]
  17. Rodriguez-Saona, L.E.; Allendorf, M.E. Use of FTIR for Rapid Authentication and Detection of Adulteration of Food. Annu. Rev. Food Sci. Technol. 2011, 2, 467–483. [Google Scholar] [CrossRef]
  18. Lohumi, S.; Lee, S.; Lee, W.; Kim, M.S.; Mo, C.; Bae, H. Detection of starch adulteration in onion powder by FT-NIR and FT-IR spectroscopy. J. Agric. Food Chem. 2014, 62, 9246–9251. [Google Scholar] [CrossRef] [PubMed]
  19. Titus, D.; Samuel, J.J.; Roopan, S.M. Chapter 12—Nanoparticle characterization techniques. In Green Synthesis, Characterization and Applications of Nanoparticles; Shukla, A.K., Iravani, S., Eds.; Matthew Deans: Chennai, India, 2019; pp. 303–319. [Google Scholar] [CrossRef]
  20. Li, X.; Zhang, Y.; He, Y. Rapid detection of talcum powder in tea using FT-IR spectroscopy coupled with chemometrics. Sci. Rep. 2016, 6, 30313. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Amsaraj, R.; Mutturi, S. Real-coded GA coupled to PLS for rapid detection and quantification of tartrazine in tea using FT-IR spectroscopy. LWT-Food Sci. Technol. 2021, 139, 110583. [Google Scholar] [CrossRef]
  22. Lohumi, S.; Joshi, R.; Kandpal, L.M.; Lee, H.; Kim, M.S.; Cho, H.; Mo, C.; Seo, Y.W.; Rahman, A.; Cho, B.K. Quantitative analysis of Sudan dye adulteration in paprika powder using FTIR spectroscopy. Food Addit. Contam. 2017, 34, 678–686. [Google Scholar] [CrossRef] [PubMed]
  23. Lohumi, S.; Lee, S.; Cho, B. Optimal variable selection for Fourier Transform infrared spectroscopic analysis of starch-adulterated garlic power. Sens. Actuators B Chem. 2015, 216, 622–628. [Google Scholar] [CrossRef]
  24. Galvin-King, P.; Haughey, S.A.; Elliott, C.T. Garlic adulteration detection using NIR and FTIR spectroscopy and chemometrics. J. Food Compos. Anal. 2021, 96, 103757. [Google Scholar] [CrossRef]
  25. Zhao, X.; Wang, W.; Ni, X.; Chu, X.; Li, Y.F.; Sun, C. Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour. Appl. Sci. 2018, 8, 1076. [Google Scholar] [CrossRef] [Green Version]
  26. Huang, H.; Liu, L.; Ngadi, M.O. Assessment of intramuscular fat content of pork using NIR hyperspectral images of rib end. J. Food Eng. 2017, 193, 29–41. [Google Scholar] [CrossRef]
  27. Isaksson, T.; Naes, T. The effect of Multiplicative Scatter Corrective (MSC) and Linearity Improvement in NIR Spectroscopy. Appl. Spectrosc. 1988, 42, 1273–1284. [Google Scholar] [CrossRef]
  28. Huand, F.; Song, H.; Guo, L.; Guang, P.; Yang, X.; Li, L.; Zhao, H.; Yang, M. Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion. Spectrochim. Acta-A Mol. Biomol. Spectrosc. 2020, 235, 118297. [Google Scholar] [CrossRef]
  29. Liu, C.; Yang, S.X.; Deng, L. A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges. Expert Syst. Appl. 2015, 42, 8497–8503. [Google Scholar] [CrossRef]
  30. Li, G.; Ren, Y.; Ren, X.; Zhang, X. Non-destructive measurement of fracturability and chewiness of apple by FT-NIRS. J. Food Sci. Technol. 2015, 52, 258–266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Li, L.; Chin, W.S. Rapid and sensitive SERS detection of melamine in milk using Ag nanocube array substrate coupled with multivariate analysis. Food Chem. 2021, 357, 129717. [Google Scholar] [CrossRef]
  32. Rios-Reina, R.; Elcoroaristizabal, S.; Ocana-Gonzalea, J.A.; Garcia-Gonzalez, D.L.; Amigo, J.M.; Callejon, R.M. Characterization and authentication of Spanish PDO wine vinegars using multidimensional fluorescence and chemometrics. Food Chem. 2017, 230, 108–116. [Google Scholar] [CrossRef] [Green Version]
  33. Yu, S.; Huang, X.; Wang, L.; Ren, Y.; Zhang, X.; Wang, Y. Characterization of selected Chinese soybean paste based on flavor profiles using HS-SPME-GC/MS, E-nose and E-tongue combined with chemometrics. Food Chem. 2022, 375, 131840. [Google Scholar] [CrossRef]
  34. Yao, K.; Sun, J.; Zhou, X.; Nirere, A.; Tian, Y.; Wu, X. Nondestructive detection for egg freshness grade based on hyperspectral imaging technology. J. Food Process Eng. 2020, 43, 13422. [Google Scholar] [CrossRef]
  35. Vapnik, V. Nonlinear Modeling. In The Support Vector Method of Function Estimation; Springer: Berlin/Heidelberg, Germany, 2018; pp. 55–85. [Google Scholar] [CrossRef]
  36. Pantazi, X.E.; Moshou, D.; Bochtis, D. Chapter 2—Artificial intelligence in agriculture. In Intelligent Data Mining and Fusion Systems in Agriculture; Charlotte Cockle: Chennai, India, 2020; pp. 17–101. [Google Scholar] [CrossRef]
  37. Ballabio, D.; Todeschini, R. Part 4: Multivariate Classification for Qualitative Analysis. In Infrared Spectroscopy for Food Quality Analysis and Control, 1st ed.; Sun, D., Ed.; Elsevier’s Science & Technology: Oxford, UK, 2009; pp. 83–103. [Google Scholar]
  38. Amirruddin, A.D.; Muharam, F.M.; Ismail, M.H.; Ismail, M.F.; Tan, N.P.; Karam, D.S. Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: Analysis of decision tree and random forest. Comput. Electron. Agric. 2020, 169, 105221. [Google Scholar] [CrossRef]
  39. Brasil, Y.L.; Cruz-Tirado, J.D.; Barbin, D.F. Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning. Food Control 2022, 131, 108418. [Google Scholar] [CrossRef]
  40. Roggo, Y.; Chalus, P.; Maurer, L.; Lema-Martinez, C.; Edmond, A.; Jent, N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharm. Biomed. Anal. 2007, 44, 683–700. [Google Scholar] [CrossRef] [PubMed]
  41. Workman, J.; Weyer, L. Practical Guide and Spectral atlas for Interpretive Near-Infrared Spectroscopy, 2nd ed.; Taylor & Francis Group LLC.: Boca Raton, FL, USA, 2012. [Google Scholar]
  42. Barbin, D.F.; de Souza Madureira Felicio, A.L.; Sun, D.W.; Nixdorf, S.L.; Hirooka, E.Y. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Res. Int. 2014, 61, 23–32. [Google Scholar] [CrossRef] [Green Version]
  43. Rieppo, L.; Saarakkala, S.; Narhi, T.; Helminen, H.J.; Jurvelin, J.S.; Rieppo, J. Application of second derivative spectroscopy for increasing molecular specificity of fourier transform infrared spectroscopic imaging of articular cartilage. Osteoarthr. Cartil. 2012, 20, 451–459. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Ribeiro, J.S.; Ferreira, M.M.C.; Salva, T.J.G. Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. Talanta 2011, 83, 1352–1358. [Google Scholar] [CrossRef] [Green Version]
  45. Esteban-Diez, I.; Gonzalez-Saiz, J.M.; Pizarro, C. Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy. Anal. Chim. Acta 2004, 525, 171–182. [Google Scholar] [CrossRef]
  46. Scholz, M.B.; Kitzberger, C.S.G.; Pereira, L.; Davrieux, F.; Pot, D.; Charmetant, P.; Leroy, T. Application of near infrared spectroscopy for green coffee biochemical phenotyping. J. Near Infrared Spectrosc. 2014, 22, 411–421. [Google Scholar] [CrossRef]
  47. Chanda, S.; Hazarika, A.K.; Choudhury, N.; Islam, S.A.; Manna, R.; Sabhapondit, S.; Tudu, B.; Bandyopadhyay, R. Support vector machine regression on selected wavelength regions for quantitative analysis of caffeine in tea leaves by near infrared spectroscopy. J. Chemom. 2019, 33, 1–15. [Google Scholar] [CrossRef]
  48. Ribeiro, J.S.; Salva, T.; Silvarolla, M.B. Prediction of a wide range of compounds concentration in raw coffee beans using NIRS, PLS and variable selection. Food Control 2021, 125, 107967. [Google Scholar] [CrossRef]
  49. Amir, R.M.; Anjum, F.M.; Khan, M.I.; Khan, M.R.; Pasha, I.; Nadeem, M. Application of Fourier Transform infrared (FTIR) Spectroscopy for the identification of wheat varieties. J. Food Sci. Technol. 2013, 50, 1018–1023. [Google Scholar] [CrossRef] [Green Version]
  50. Wang, N.; Fu, Y.; Lim, L.T. Feasibility Study on Chemometric Discrimination of Roasted Arabica Coffees by Solvent Extraction and Fourier Transform infrared Spectroscopy. J. Agric. Food Chem. 2019, 59, 3220–3226. [Google Scholar] [CrossRef]
  51. Wang, J.; Jun, S.; Bittenbender, H.C.; Gautz, L.; Li, Q.X. Fourier Transform infrared Spectroscopy for Kona Coffee Authentication. J. Food Sci. 2009, 74, C385–C391. [Google Scholar] [CrossRef]
  52. Reis, N.; Franca, A.S.; Oliveira, L.S. Performance of diffuse reflectance infrared Fourier transform spectroscopy and chemometrics for detection of multiple adulterants in roasted and ground coffee. LWT-Food Sci. Technol. 2013, 53, 395–401. [Google Scholar] [CrossRef] [Green Version]
  53. Craig, A.P.; Franca, A.S.; Oliveira, L.S.; Irudayaraj, J.; Ileleji, K. Application of elastic net and infrared spectroscopy in the discrimination between defective and non-defective roasted coffees. Talanta 2014, 128, 393–400. [Google Scholar] [CrossRef] [Green Version]
  54. Kemsley, E.K.; Ruault, S.; Wilson, R.H. Discrimination between Coffea arabica and Coffea canephora variant robusta beans using infrared spectroscopy. Food Chem. 1995, 54, 321–326. [Google Scholar] [CrossRef]
Foods 11 03122 g001 550
Figure 1. Schematic view of: (A) NIR-HSI; and (B) FTIRs system.
Figure 1. Schematic view of: (A) NIR-HSI; and (B) FTIRs system.
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Figure 2. The average absorbance spectra of pure Arabica and pure Robusta using NIR-HSI: (A) Original spectra; (B) 2nd derivative spectra.
Figure 2. The average absorbance spectra of pure Arabica and pure Robusta using NIR-HSI: (A) Original spectra; (B) 2nd derivative spectra.
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Figure 3. The average original spectra of pure Arabica and pure Robusta using FTIRs.
Figure 3. The average original spectra of pure Arabica and pure Robusta using FTIRs.
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Figure 4. Score plots of PCA for: (A) pure Arabica and pure Robusta; (B) pure Arabica and Arabica with Robusta using spectral data of NIR-HSI.
Figure 4. Score plots of PCA for: (A) pure Arabica and pure Robusta; (B) pure Arabica and Arabica with Robusta using spectral data of NIR-HSI.
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Figure 5. Score plots of PCA for: (A) pure Arabica and pure Robusta; (B) pure Arabica and Arabica with Robusta using spectral data of FTIRs.
Figure 5. Score plots of PCA for: (A) pure Arabica and pure Robusta; (B) pure Arabica and Arabica with Robusta using spectral data of FTIRs.
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Figure 6. The confusion matrices of classification in the calibration set by NIR-HSI (A), the prediction set by NIR-HSI (B), the calibration set by FTIRs (C), and the prediction set by FTIRs (D): Class 0 = pure Arabica, Class 1 = Arabica with Robusta.
Figure 6. The confusion matrices of classification in the calibration set by NIR-HSI (A), the prediction set by NIR-HSI (B), the calibration set by FTIRs (C), and the prediction set by FTIRs (D): Class 0 = pure Arabica, Class 1 = Arabica with Robusta.
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Figure 7. The scatter plot of actual and predicted adulterant concentration in: (A) the calibration set by NIR-HSI; (B) the prediction set by NIR-HSI; (C) the calibration set by FTIRs; (D) the prediction set by FTIRs.
Figure 7. The scatter plot of actual and predicted adulterant concentration in: (A) the calibration set by NIR-HSI; (B) the prediction set by NIR-HSI; (C) the calibration set by FTIRs; (D) the prediction set by FTIRs.
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Table
Table 1. The characteristics of samples in the calibration set and the prediction set for SVMC and SVMR using NIR-HSI and FTIRs.
Table 1. The characteristics of samples in the calibration set and the prediction set for SVMC and SVMR using NIR-HSI and FTIRs.
TechniquesItemsModel
SVMC 5SVMR 6
Cal 7Pred 8CalPred
NIR-HSI 1Number of samples20610213666
% Concentration of Robusta 30–10–10–1001–98
Mean (%)0.640.6550.2449.50
SD 4 (%)0.480.4829.5428.79
FTIRs 2Number of samples20610213666
% Concentration of Robusta0–10–10–1001–98
Mean (%)0.640.6550.2449.50
SD (%)0.480.4829.5428.79
1 NIR hyperspectral imaging. 2 Fourier transform infrared spectroscopy. 3 Support vector machine classification. 4 Support vector machine regression. 5 Concentration of Robusta ground roasted coffee. 6 Standard deviation. 7 Calibration. 8 Prediction.
Table
Table 2. Performance of SVMC models for classification between pure Arabica and the Arabica with Robusta using NIR-HSI and FTIRs.
Table 2. Performance of SVMC models for classification between pure Arabica and the Arabica with Robusta using NIR-HSI and FTIRs.
ModelTechniqueNumber of SamplesPre-Treatment% Accuracy% Specificity% Sensitivity% Error Rate
CalPredCal 6Pred 7CalPredCalPredCalPred
SVMC 1NIR-HSI 2206102SNV 499.0398.0410010097.3794.740.971.96
FTIRs 32061021st derivative 5 + SNV97.0997.0610010092.5092.312.913
1 Support vector machine classification. 2 NIR hyperspectral imaging. 3 Fourier transform infrared spectroscopy. 4 Standard normal variate transformation. 5 Savitzky-Golay first derivative. 6 Calibration. 7 Prediction.
Table
Table 3. Results of SVMR models for detection of concentration of RC in the adulterated AC with RC using NIR-HSI and FTIRs.
Table 3. Results of SVMR models for detection of concentration of RC in the adulterated AC with RC using NIR-HSI and FTIRs.
ModelTechniqueNumber of SamplesPre-TreatmentR2cR2cvR2pRMSEC 9
(%)		RMSECV 10
(%)		RMSEP 11
(%)
Cal 4Pred 5
SVMR 1NIR-HSI 2178881st derivative 60.9700.9530.9584.665.865.35
FTIRs 3131632nd derivative 7 + SNV 80.9650.9130.9515.859.046.96
1 Support vector machine regression. 2 NIR hyperspectral imaging. 3 Fourier transform infrared spectroscopy. 4 Calibration. 5 Prediction. 6 Savitzky-Golay first derivative. 7 Savitzky-Golay second derivative. 8 Standard normal variate transformation. 9 Root mean square error of calibration. 10 Root mean square error of cross-validation 11 Root mean square error of prediction.
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Sahachairungrueng, W.; Meechan, C.; Veerachat, N.; Thompson, A.K.; Teerachaichayut, S. Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy. Foods 2022, 11, 3122. https://doi.org/10.3390/foods11193122

AMA Style

Sahachairungrueng W, Meechan C, Veerachat N, Thompson AK, Teerachaichayut S. Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy. Foods. 2022; 11(19):3122. https://doi.org/10.3390/foods11193122

Chicago/Turabian Style

Sahachairungrueng, Woranitta, Chanyanuch Meechan, Nutchaya Veerachat, Anthony Keith Thompson, and Sontisuk Teerachaichayut. 2022. "Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy" Foods 11, no. 19: 3122. https://doi.org/10.3390/foods11193122

APA Style

Sahachairungrueng, W., Meechan, C., Veerachat, N., Thompson, A. K., & Teerachaichayut, S. (2022). Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy. Foods, 11(19), 3122. https://doi.org/10.3390/foods11193122

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