Authors:
Hoa Xuan Mac Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
Faculty of Food Science and Technology, Ho Chi Minh City University of Industry and Trade, Ho Chi Minh 700000, Vietnam

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Nga Thi Thanh Ha Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
Faculty of Food Science and Technology, Ho Chi Minh City University of Industry and Trade, Ho Chi Minh 700000, Vietnam

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Lien Le Phuong Nguyen Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary

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László Baranyai Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary

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Abstract

In this work, the simulated adulteration of coconut drink by dilution with water was investigated using laser-light backscattering (LLB) imaging. The laser vision system consisted of six low power laser modules, emitting 1 mm diameter beams at wavelengths of 532, 635, 780, 808, 850 and 1,064 nm. The backscattering images were acquired by a grey scale camera with 12 bit resolution. Eight parameters were extracted to describe the backscattering profile. The methods of linear discriminant analysis (LDA) and partial least squares (PLS) regression were performed on LLB parameters for classifying and predicting dilution level of adulterated coconut drink samples. Based on the results, LLB signals responded sensitively to adulteration. LDA results showed that adulterated samples were correctly recognized with accuracies between 60 and 100%. PLS models were able to estimate the adulteration level of samples with coefficients of determination of 0.57–0.97 in validation. This result demonstrated the potential of laser-light backscattering imaging as a rapid and non-destructive optical technique for evaluation of coconut drink adulteration.

Abstract

In this work, the simulated adulteration of coconut drink by dilution with water was investigated using laser-light backscattering (LLB) imaging. The laser vision system consisted of six low power laser modules, emitting 1 mm diameter beams at wavelengths of 532, 635, 780, 808, 850 and 1,064 nm. The backscattering images were acquired by a grey scale camera with 12 bit resolution. Eight parameters were extracted to describe the backscattering profile. The methods of linear discriminant analysis (LDA) and partial least squares (PLS) regression were performed on LLB parameters for classifying and predicting dilution level of adulterated coconut drink samples. Based on the results, LLB signals responded sensitively to adulteration. LDA results showed that adulterated samples were correctly recognized with accuracies between 60 and 100%. PLS models were able to estimate the adulteration level of samples with coefficients of determination of 0.57–0.97 in validation. This result demonstrated the potential of laser-light backscattering imaging as a rapid and non-destructive optical technique for evaluation of coconut drink adulteration.

Introduction

Food adulteration is a prohibited act to gain quick economic profit, in which the quality of original food is deliberately reduced by either adding inferior substances or removing valuable components (FDA, 2009). Adulteration can mislead the consumers about the quality of products (Aouadi et al., 2022; Bodor et al., 2023). Moreover, allergic reactions may occur, when consumers do not know the exact components of foods they are consuming. Every year, the economic damage caused by food adulteration was estimated to be around €30 billion for the global market (Steinberg et al., 2019) and €8 to €12 billion for the European market (European Commission, 2020).

Fruit-based drinks are rich in nutrients and bioactive substances, such as carbohydrates, proteins, vitamins, antioxidants and minerals. Among fruit-based beverages, coconut drink is known for not only its nutritional value but also as an isotonic drink thanks to its excellent rehydration index and blood glucose response (Intan Kailaku et al., 2015). Fruit-based beverages have become one of the most frequently adulterated foods because of their high market value. The practice of adulteration of fruit-based drinks is mainly performed through dilution with water, addition of sugar and food additives, and addition of less valuable juices; of which water dilution is the most common one because it is simple and inexpensive.

In order to protect the consumers from low quality beverages, numerous studies have been conducted to develop non-destructive techniques for detection of adulterated products. These methods can offer several advantages over chemical approaches, including quick data acquisition, no or less requirement for sample preparation, and environment protection (Soós et al., 2014; Vitális et al., 2020; Zaukuu et al., 2022). For instance, infrared (IR) spectroscopy was applied for quantifying addition of sugar to orange juice (Ellis et al., 2016), detecting artificial sweeteners in commercial fruit juices (Mabood et al., 2018), identifying exogenous carbohydrates in coconut water (Teklemariam et al., 2021), and classifying grape nectars adulterated with apple juice and cashew juice (Miaw et al., 2018). Sitorus et al. (2022) used mid-infrared spectroscopy in range of 4,000–16,702 nm for detecting adulteration of coconut milk with distilled water (Sitorus et al., 2022). In addition, a combination of electronic tongue and electronic nose was used to detect adulterants in tomato juices (Hong et al., 2014). Moreover, digital image analysis was applied to identify adulteration in Physalis juice (Licodiedoff et al., 2013), and orange juice (Stinco Scanarotti et al., 2014).

Among non-destructive approaches, LLB imaging is a novel technique for food quality control, which has found its application in determining quality attributes of fruits and crops during ripening and storage (Adebayo et al., 2017; Ali et al., 2017; Sanchez et al., 2020), detecting defects in fruits (Mollazade et al., 2017; Wu et al., 2020), monitoring quality parameters of fruits in postharvest processes (Hashim et al., 2013; Dénes et al., 2013). The principle of LLB imaging is based on the phenomenon of light backscattering that occurs when the light photons interact with internal components of food matrices and scatter back to the surface (Mollazade et al., 2012). The backscattering profile at the surface of food matrices is related to their quality attributes (Lu et al., 2006). Thus, measurement and analysis of the backscattering profile of foods could provide information about their quality.

LLB imaging was mainly used for quality evaluation of agricultural produces, its application on transparent or partially opaque materials is very limited. To the best of our knowledge, there is no literature on application of LLB imaging for detecting adulteration of coconut drinks. Thus, the main objective of this work aimed at investigating the applicability of laser light backscattering imaging technique for detection of adulterated coconut drink.

Materials and methods

Materials

Ready-to-serve coconut drink was purchased from the distributor SPAR Hungary Ltd. (Bicske, Hungary). The product ingredients mainly included water (92%), sugar, coconut milk (3%), and stabilizers. The samples were stored at room temperature (approximately 20 °C) before the experiment.

Simulation of adulteration

Simulation of adulteration of coconut drink was performed at five dilution levels by adding distilled water to the original coconut drink at 5% (v/v) increment to make five adulteration levels (0 as original drink, 5, 10, 15, and 20%).

Acquisition of laser backscattering images

The laser-induced diffuse reflectance system consisted of six low power laser modules, emitting 1 mm diameter beams at wavelengths of 532, 635, 780, 808, 850, and 1,064 nm. Direct reflectance was avoided by adjusting the incident angle of the light beam at 15°. Backscattering images were taken using a 12 bit camera (MV1-D1312, Photonfocus, Lachen, Switzerland) with 0.113 mm/pixel spatial resolution. To improve the signal-to-noise ratio and prevent the interference of environmental light during the measurement, image acquisition was conducted in a dark chamber. Figure 1 presents the setup of the vision system.

Fig. 1.
Fig. 1.

Setup of the laser vision system

Citation: Progress in Agricultural Engineering Sciences 19, S1; 10.1556/446.2023.00082

After being well stirred, samples were poured into a Petri dish with 3.5 mm height. For measurement of each adulteration level, three samples were used. Image acquisition of each sample was carried out using six laser light sources sequentially. Three images were captured for each wavelength and saved as binary image data. As a result, 18 images were obtained for each sample. The measurement of a sample lasted for less than 1 min; therefore, the result was not affected by sedimentation.

Method of image analysis

The captured images were processed using an algorithm, which was written in GNU Octave software (version 4.4.1). The binary images were converted to greyscale images (Fig. 2). Light intensity (0–4,095) was normalized to the range of 0–1. The 1 dimensional scattering profile was computed by averaging normalized intensity (Fig. 3). The LLB profile had a symmetric shape with highest intensity at the light incident point. As the distance from the incident point increased, the light intensity declined gradually. The LLB parameters were extracted (Fig. 4), including the peak widths at three selected intensity levels of 75%, 50%, 25% (D75, D50, D25); and the illuminated areas at selected intensity level of 50% (A50), the ring of 25–75 (A2575). The sharpness and the shape of the profile were described using the ratios of the widths (D25/D75, D50/D75) and the areas (A50/A2575).

Fig. 2.
Fig. 2.

Example of a backscattering image

Citation: Progress in Agricultural Engineering Sciences 19, S1; 10.1556/446.2023.00082

Fig. 3.
Fig. 3.

1 dimensional profile of LLB signal

Citation: Progress in Agricultural Engineering Sciences 19, S1; 10.1556/446.2023.00082

Fig. 4.
Fig. 4.

Description of extracted backscattering parameters

Citation: Progress in Agricultural Engineering Sciences 19, S1; 10.1556/446.2023.00082

Statistical analysis

Statistical analyses were performed using the free software R (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria) and RStudio (version 2023.06.1 + 524, Posit Software PBC, Boston, MA, USA). Two-way analysis of variance (ANOVA) was used to test significance of effects of adulteration and wavelength on LLB parameters (P ≤ 0.05). Correlation analysis was performed to determine the relationship of LLB parameters and adulteration level. The classification ability of LLB imaging over adulterated samples was evaluated using linear discriminant analysis (LDA). The percentage of correctly classified data in validation was used to evaluate the classification power. Partial least squares (PLS) regression was used to predict the adulteration level. PLS model performance was evaluated based on coefficient of determination (R2) and root mean squared error (RMSE) in validation. Both LDA and PLS models were trained using 80% randomly selected data and validated using the remaining 20% data.

Results and discussion

Change of LLB parameters

The significant changes of LLB parameters according to dilution level and wavelength were evaluated using two-way ANOVA at significance levels of P < 0.05 (Table 1). The result showed that adulteration had significant effects on parameters related to the peak widths and the illuminated areas of LLB profiles, including D75, D50, D25, A50, and A2575 (P < 0.05). Meanwhile, the parameters describing the sharpness and the shape of signals (D75/D25, D50/D25, A50/A2575) did not undergo any significant changes (P > 0.05). Significant variation of LLB signals by wavelength was also observed. D25 (F = 184.99) and A2575 (F = 151.36) were the most sensitive parameters to wavelength, whereas the two ratios of D50/D75 (F = 2.86) and D25/D75 (F = 9.74) had the lowest response. Furthermore, all parameters showed no significant interaction effects of factors (P > 0.05), except the ratio A50/A2575 (P < 0.05).

Table 1.

ANOVA F-values of LLB parameters

FactorLLB parameterMean SquareFP
WavelengthD75319.65100.61<0.001
D50691.59138.13<0.001
D252,606.53184.99<0.001
D50/D750.062.86<0.05
D25/D750.959.74<0.001
A50625,015.05118.63<0.001
A25754,819,088.13151.36<0.001
A50/A25750.0335.48<0.001
DilutionD7512.033.79<0.05
D5029.225.84<0.001
D2571.005.04<0.001
D50/D750.010.690.60
D25/D750.070.700.60
A5027,483.435.22<0.001
A2575201,112.936.32<0.001
A50/A25750.000.540.71
Wavelength × dilutionD752.920.920.57
D504.950.990.49
D259.170.650.85
D50/D750.010.580.91
D25/D750.070.670.84
A505,130.780.970.51
A257531,400.860.990.49
A50/A25750.001.94<0.05

The relationship of measured backscattering signals and adulteration level was explored using correlation analysis (Table 2). The parameters for the widths and areas of profile (D75, D50, D25, A50, and A2575) had stronger correlation with adulteration level than their ratios (D25/D75, D50/D75, and A50/A2575). The strongest correlations were observed for A50 and A2575 at 635, 780, 808, 850, and 1,064 nm with absolute values of correlation coefficient up to 0.96. As a colloidal system with oil-in-water emulsion, the coconut drink contains fat droplets which mainly scatter the light. In diluted samples, the reduction in concentration of fat can be the reason for the change in light backscattering. This assumption is supported by Qin et al. (2007), who observed a correlation of reduced scattering coefficient and fat content of milk (Qin et al., 2007). Additionally, according to Mie's scattering theory, the scattering intensity is correlated to the refractive index ratio between scattering particles and liquids surrounding the particles (Mie, 1908). In this work, the addition of water to coconut drink samples changed their refractive indices, thus varying the light scattering.

Table 2.

Correlation coefficient of LLB parameters and adulteration level

LLB parameterWavelength
532 nm635 nm780 nm808 nm850 nm1,064 nm
D75−0.22−0.8−0.85−0.61−0.17−0.47
D50−0.25−0.84−0.75−0.6−0.63−0.57
D25−0.1−0.77−0.59−0.6−0.79−0.78
A50−0.3−0.95−0.96−0.9−0.62−0.46
A2575−0.08−0.94−0.90−0.9−0.91−0.92
D50/D75−0.060.220.16−0.02−0.440.11
D25/D750.180.490.03−0.14−0.450.2
A50/A2575−0.71−0.810.550.080.480.27

Based on the above findings, it can be concluded that LLB parameters had high responses to adulteration and this proposed technique is promising for detecting adulteration in studied coconut drink.

Detection of adulteration

In this work, the feasibility of LLB imaging for detection of adulteration in coconut drink was investigated using the methods of LDA and PLS regression. LDA and PLS models were built using LLB parameters of single wavelength (8 parameters per wavelength) and their combination of all six wavelengths (48 parameters). All models were trained using 80% randomly selected data and validated using the remaining 20%. The quality of models was evaluated using calculated metrics in validation.

For classification, Table 3 illustrates the average accuracy of LDA models. Based on the result, LDA models of 635, 780, 808, 850, 1,064 nm achieved good accuracy of 80%. Meanwhile, the LDA model of 532 nm performed with the lowest success rate of 60%. The highest classification power was observed for the LDA model of all wavelengths with the accuracy of 100%. Regarding adulteration level quantification, the performance of PLS models is given in Table 4. The number of latent variables (LV) varied in range of 2–3, which was determined at minimum values of RMSE. In general, PLS models achieved R2 = 0.57 – 0.97 and RMSE = 0.309 – 1.651%. The quality of a model is considered excellent when its coefficient of determination is greater than 0.90 (Cuadrado et al., 2005). For single wavelength, the best performances of the PLS model were observed at 780 nm (R2 = 0.90, RMSE = 0.458%) and 1,064 nm (R2 = 0.93, RMSE = 0.545%). Meanwhile, the PLS model at 532 nm was not appropriate for determination of adulteration level due to its poor performance (R2 = 0.57, RMSE = 1.651%). Similar to LDA classification, the use of multispectral data with combination of all wavelengths resulted in an increase of PLS model accuracy with R2 = 0.97 and RMSE = 0.352%.

Table 3.

Performance of LDA classification

WavelengthClassification accuracy
532 nm60%
635 nm80%
780 nm80%
808 nm80%
850 nm80%
1,064 nm80%
All wavelengths100%
Table 4.

Performance of PLS models

WavelengthLVR2RMSE (%)
532 nm20.571.651
635 nm20.810.309
780 nm20.900.458
808 nm20.810.479
850 nm20.830.494
1,064 nm20.930.545
All wavelengths30.970.352

Our findings demonstrated that LLB imaging is capable of identifying adulteration by water addition in coconut drink used in present study. The proposed technique achieved similar performance with the mid-infrared spectroscopy (4,000–16,702 nm), applied by Sitorus et al. (2022) for predicting adulteration level in water-adulterated coconut milk. Their PLS model obtained high accuracy of prediction with R2 > 0.90 and RMSE <2% (Sitorus et al., 2022). For adulteration detection in transparent and partially opaque products, application of LLB imaging is very limited. Recently, a very first research was performed by Hencz et al. (2022) who investigated the feasibility of LLB imaging in detecting adulterants in red and white wines (Hencz et al., 2022). For detection of water dilution, their LDA models (success rate ≤76.67%) obtained poorer accuracy than ours. Moreover, the prediction error of their general linear model (20.39%) was much higher than that of our PLS models (<2%). The results of this work suggest that the proposed method can be used for adulteration detection of coconut drink.

Conclusion

The applicability of LLB imaging for detection of coconut drink adulteration was confirmed. The results of LDA showed that the proposed technique was able to recognize the diluted coconut drink used in the present work. All LDA models obtained high accuracy of discrimination with success rate up to 100%. LDA model produced no misclassification in combination of all wavelengths. PLS models obtained high performance with R2 > 0.80 and RMSE <1% using backscattering signals at single wavelengths of 635, 780, 808, 850 and 1,064 nm. The quality of the PLS model was improved when signals of all wavelengths were used (R2 = 0.97, RMSE = 0.352%). Based on the results, we suggest multispectral measurement in detection of adulteration with a combination of 532, 635, 780, 808, 850, 1,064 nm. However, our work has a limitation since its findings are based on the measurement of a small sample from one manufacturer. More studies are needed with larger samples to demonstrate the promising application of the technique in real life.

Acknowledgement

The authors acknowledge the Doctoral School of Food Science of Hungarian University of Agriculture and Life Sciences for the support in this study.

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The author instruction is available in PDF.
Please, download the file from HERE.

 

 

Senior editors

Editor(s)-in-Chief: Felföldi, József

Chair of the Editorial Board Szendrő, Péter

Editorial Board

  • Beke, János (Szent István University, Faculty of Mechanical Engineerin, Gödöllő – Hungary)
  • Fenyvesi, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Szendrő, Péter (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Felföldi, József (Szent István University, Faculty of Food Science, Budapest – Hungary)

 

Advisory Board

  • De Baerdemaeker, Josse (KU Leuven, Faculty of Bioscience Engineering, Leuven - Belgium)
  • Funk, David B. (United States Department of Agriculture | USDA • Grain Inspection, Packers and Stockyards Administration (GIPSA), Kansas City – USA
  • Geyer, Martin (Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Horticultural Engineering, Potsdam - Germany)
  • Janik, József (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Kutzbach, Heinz D. (Institut für Agrartechnik, Fg. Grundlagen der Agrartechnik, Universität Hohenheim – Germany)
  • Mizrach, Amos (Institute of Agricultural Engineering. ARO, the Volcani Center, Bet Dagan – Israel)
  • Neményi, Miklós (Széchenyi University, Department of Biosystems and Food Engineering, Győr – Hungary)
  • Schulze-Lammers, Peter (University of Bonn, Institute of Agricultural Engineering (ILT), Bonn – Germany)
  • Sitkei, György (University of Sopron, Institute of Wood Engineering, Sopron – Hungary)
  • Sun, Da-Wen (University College Dublin, School of Biosystems and Food Engineering, Agriculture and Food Science, Dublin – Ireland)
  • Tóth, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)

Prof. Felföldi, József
Institute: MATE - Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Department of Measurements and Process Control
Address: 1118 Budapest Somlói út 14-16
E-mail: felfoldi.jozsef@uni-mate.hu

Indexing and Abstracting Services:

  • CABI
  • ERIH PLUS
  • SCOPUS

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
9
Scimago
Journal Rank
0.191
Scimago Quartile Score

Environmental Engineering (Q4)
Industrial Manufacturing Engineering (Q3)
Mechanical Engineering (Q3)

Scopus  
Scopus
Cite Score
1.1
Scopus
CIte Score Rank
General Agricultural and Biological Sciences 141/213 (34th PCTL)
Agricultural and Biological Sciences 104/147 (29th PCTL)
Industrial and Manufacturing Engineering 261/355 (26th PCTL)
Mechanical Engineering 494/631 (21st PCTL)
Environmental Engineering 145/184 (21st PCTL)
 
Scopus
SNIP
0.222

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
8
Scimago
Journal Rank
0,141
Scimago Quartile Score Environmental Engineering (Q4)
Industrial and Manufacturing Engineering (Q4)
Mechanical Engineering (Q4)
Scopus  
Scopus
Cite Score
0,8
Scopus
CIte Score Rank
Industrial and Manufacturing Engineering 261/338 (Q4)
Environmental Engineering 138/173 (Q4)
Mechanical Engineering 495/601 (Q4)
Scopus
SNIP
0,381

2020  
Scimago
H-index
8
Scimago
Journal Rank
0,197
Scimago
Quartile Score
Environmental Engineering Q4
Industrial and Manufacturing Engineering Q3
Mechanical Engineering Q4
Scopus
Cite Score
33/69=0,5
Scopus
Cite Score Rank
Environmental Engineering 126/146 (Q4)
Industrial and Manufacturing Engineering 269/336 (Q3)
Mechanical Engineering 512/596 (Q4)
Scopus
SNIP
0,211
Scopus
Cites
53
Scopus
Documents
41
Days from submission to acceptance 122
Days from acceptance to publication 40
Acceptance rate 86%

 

2019  
Scimago
H-index
6
Scimago
Journal Rank
0,123
Scimago
Quartile Score
Environmental Engineering Q4
Industrial and Manufacturing Engineering Q4
Mechanical Engineering Q4
Scopus
Cite Score
18/33=0,5
Scopus
Cite Score Rank
Environmental Engineering 108/132 (Q4)
Industrial and Manufacturing Engineering 242/340 (Q3)
Mechanical Engineering 481/585 (Q4)
Scopus
SNIP
0,211
Scopus
Cites
13
Scopus
Documents
5

 

Progress in Agricultural Engineering Sciences
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Progress in Agricultural Engineering Sciences
Language English
Size B5
Year of
Foundation
2004
Volumes
per Year
1
Issues
per Year
1
Founder Magyar Tudományos Akadémia  
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1786-335X (Print)
ISSN 1787-0321 (Online)

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