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Orange juices are a popular refreshment beverage and
nutrition source. However, the adulteration of pure juice has now become a
common practice. In this study, near infrared spectroscopy was applied as a
means to determine whether different orange juice samples had been adulterated with
sugar solution or not. For this purpose, 10 samples of 100% fresh juice and80
samples with different concentrations of sugar solutionswere created in a
laboratory setting. Each sample was scanned with an FT-NIR spectrometer. A PLS
regression model tested by validation set to predict the sugar-added content of
juice samples provided R2, RMSEP and a bias of 92.06%, 0.0361 g/ml,
and -0.0041 g/ml, respectively and for water-added 87.68%, 7.37%, and -1.26%,
respectively. These results confirm that an NIR-based protocol could be applied
for sugar adulteration in orange juice.
INTRODUCTION
Due to increasing temperatures and
the desire for healthier diets, cool fruit juices have become popular
particularly in Bangkok and near-by provinces. Orange juice is rich in phenolic
compounds and ascorbic acid [1].Because of its phenolic content, orange juice
has known antioxidant properties [2].In addition, significant amounts of
L-ascorbic acid or vitamin C is contained in oranges, making them an important
source of nutrition. Indeed, the nutrition content found of oranges is commonly used
as a nutritional index.
Due to the size and value of the
commercial fruit juice market, the adulteration of the product has been widely
practiced, especially in locations along busy roads and intersections in
Thailand.Orange juice vendors are found scattered throughout Bangkok and
near-by Provinces. It is a widely held belief that adulteration of the orange
juice by the addition of sugar solution is a common practice. Therefore the
ability to detect for the adulteration of orange juice with sugar solution is
clearly needed to protect consumers.
Near infrared spectroscopy (NIR) is a non-destructive technique that can be used to rapidly evaluate chemical constituents of materials. Rodriguez-Saona et al. [3] developed an FT-NIR model to predict the sugar levels in fruit juices (apple and orange from department stores) and compared it with HPLC and standard enzymatic techniques. The models generated from transmittance spectra gave the best performance with a standard error of prediction (SEP) < 0.10% and an R2 of 99.9%, with the ability to accurately and precisely predict the sugar levels in juices.
Hong and Wang [4] studied the authentication of fresh cherry tomato juices adulterated with different levels of overripe tomato juices: 0 – 30%. Their study indicated that the prediction performances by fusion approaches were better than by the sole usage of an e-nose or e-tongue method; yet classification and prediction performances based on different fusion approaches vary. Boggia et al. [5] proposed a strategy based on UV-VIS spectroscopy for the detection of filler juices and water added to pomegranate juices.Visible and NIR spectroscopy
have also been used to determine the levels of important nutrients in orange juice
including citric acid and tartaric acid. The correlation coefficients (r) and
root mean squares error of prediction (RMSEP) in the best model were 0.944 and
0.596 for citric acid and 0.930 and 0.013 for tartaric acid [6]. For soluble
solids contents (SSC) and pH the correlation coefficients (r),standard error of
prediction and RMSEP for SSC were 0.98, 0.68, and 0.73 for SSC and 0.96, 0.06,
and 0.06 for pH, respectively [7]. Determination of glucose, fructose, sucrose
and citric and malic acids in orange juices were carried out using dry extract
samples and the ability of calibration models was acceptable in comparison with
the reference methods [8]. The classification accuracy for prochloraz residue
was found to be 100% [9].
In addition, 3D-front-face
fluorescence spectroscopy has been used to assess the adulteration of orange
juice by grapefruit juice at percentages as low as 1% [10]. Principal Component
Analysis (PCA) was applied to a set of physicochemical variables and the
addition of sugar were investigated. Detecting adulterations started from
approximately 15%. The rapid automated screening technique Curie-point
pyrolysis mass spectrometry (PyMS) was used to detect a 10% (w/v) beet sucrose
solution adulterated with freshly squeezed orange juice over the range 0-20% (or
0-20 g 1-1 of added sucrose) and provided calibration models which gave
excellent predictions for sucrose
adulteration levels below 1% [12].
The applicability of rapid
analytical methods, such as NIR, for fraud detection in fruit juice, and in
particular orange juice is of most interest to researchers and government
officials. Therefore, the objective of this research was to assess the
application FT-NIR spectroscopy for the determination of the adulteration of
orange juice with sugar solution at different concentrations.
MATERIALS AND
METHODS
Samples and
Adulteration of Orange Juice with Sugar Solution
There were 10 samples of 100%
fresh juices squeezed from oranges (Citrustangerina) bought from a main
agricultural distribution market in Pathumthani Province, Thailand. Citrus
tangerina, referred to as “Kiew Wan” in Thailand, is a popular variety used for making juice for
sale. Before squeezing, the fruit was cut in half, with 2-3 halves being
squeezedat a time without peeling or seed removal.Eighty 200 ml samples of mixed
pure juice, sugar solutions and water with different concentrations by volume
were prepared (Table 1). There were 5 samples for each level of
adulteration.The sugar solution concentration used was 50% w/w.
Near Infrared
Scanning
Each sample was transferred into a
glass vial of 22 mm diameter and covered with a stainless steel transflection
plate. This provided a 2 mm optical path length, Samples were then scanned
between 12500-3600 cm-1 with a nominal resolution of 8 cm-1, accumulating 32
scans per spectrum using a background of the gold. The scanning was performed
at room temperature(25 ± 1 °C) using a Multi-Purpose Analyzer (MPA- FT-NIR
spectrometer, Bruker, Bremen, Germany).
Spectrum Pre-treatment and NIR Spectroscopy Model
Establishment
RESULTS AND DISCUSSION
Table 2 shows the minimum (Min), maximum (Max), mean, and standard deviation (SD) of sugar-added and water-added in fresh orange juice samples in calibration set and validation set.
The scatter plot in Figure 1 shows the prediction data and the reference data for the sugar adulterated samples. The best model was developed using the vector normalization (SNV) method in the range of 6102 - 5446.3 cm-1 leading to a coefficient of determination (R2), root mean square error of prediction (RMSEP), a bias and residual predictive deviation (RPD) of 92.06%, 0.0361%, -0.004%and 3.57,respectively. Table 3 shows the prediction statistics associated with the PLS model of the adulterated samples.
In addition to a very low error of prediction, the R2 of between 92-96% implies that the model is sufficiently predictive to be used in applications including quality assurance [13]. The RPD is calculated from the ratio between the standard deviation of reference values in the validation set and the standard error of prediction. The RPD of 3.1-4.9 implies that the model is sufficient for screening [13].
Figure 2 shows a plot of the regression coefficients for the model generated on the sugar adulterated samples while Figure 3 shows the corresponding X-loadings. The large regression coefficients and X-loadings indicate molecular vibration bands that are important in defining the PLS model.Table 4 contains data for the large peaks in the regression coefficient plot and X-loading plot and corresponds to important bond vibrations. It was apparent from the initial analysis that vibrations due to water effect were not important whereas those of cellulose and other C-H bonds were important. This appears to be a reflection of the fact that pure, fresh orange juice contains some degree of pulp material.The scatter plot showing the reference data and the prediction data for the
water added model is shown in Figure 4. The best model developed using
non-pre-treated spectra in the range of 9403.8 - 7498.3 cm-1 led to an R2,
RMSEP, a bias and RPD of 87.68%, 7.37%, -1.26% and 2.89, respectively. Table 5
shows the prediction statistics associated with the PLS model built on the
water-adulterated orange juice samples. The model displays R2 of between
0.83-0.90 implying that the model is usable with caution for most applications,
including research [13]. The RPD of between 2.4-3.0 implies that the model is suitable
for rough for screening [13].
Figure 5 shows a plot
of the regression coefficients for the water adulterated model while Figure 6 shows a plot of the X-loadings. The
absorption bands with large X-loadings and regression coefficients are described in more detail in Table 6. It was again obvious that the water band
vibration had no effect on the model, instead being relianton cellulose and
other C-H vibration.
CONCLUSION
ACKNOWLEDGMENT
The authors would like to express their
gratitude to the Faculty of Engineering, King Mongkut’s Institute of Technology
Ladkrabang for financial support for this research and to the Near Infrared
Spectroscopy Research Center for Agricultural Product and Food (www.nirsresearch.com)
for providing the instruments.
REFERENCES
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5. Boggia R, Casolino MC, Hysenaj V, Oliveri P, Zunin PA (2013) Screening method based on UV–Visible spectroscopy and multivariate analysis to assess addition of filler juices and water to pomegranate juices. Food Chem 140:735–741.doi:10.1016/j.foodchem.2012.11.020
6. Cen H, Bao Y, He Y, Sun D-W (2007) Visible and near infrared spectroscopy for rapid detection of citric and tartaric acids in orange juice. J Food Eng 82 (2):253-260.doi:10.1016/j.jfoodeng.2007.02.039
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9. Zhang Y,Xiang B,Dong, Y,Xu J(2013) Rapid determination of prochloraz in orange juice by near-infrared spectroscopy. Anal Lett 46(17):2739-2751.doi:10.1080/00032719.2013.811678
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12. Goodacre R, Hammond D, Kell DB (1997) Quantitative analysis of the adulteration of orange juice with sucrose using pyrolysis mass spectrometry and chemometrics. J Anal ApplPyrol 40-41:135-158.doi:10.1016/S0165-2370(96)00973-4
13. Williams P (2007) Near-infrared technology–Getting the best out of light (5thed.). A short course in the practical implementation of near-infrared spectroscopy for the user. PDK Grain, Nanaimo, Canada: p 5-8.
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