Approaches to Adulteration Detection in Instant Coffees using Infrared Spectroscopy and Chemometrics

J Sci Food Agric 1996,71,359-366

ADDroaches to Adulteration Detection in Instant ).I

Coffees using Infrared Spectroscopy and Chemome trics R Briandet, E K Kemsley* and R H Wilson Institute of Food Research, Norwich Laboratory, Norwich Research Park, Colney, Norwich, NR4 7UA, UK (Received 29 September 1995; revised version received 8 January 1996; accepted 8 February 1996)

Abstract: Fourier transform infrared (FTIR) spectroscopy is examined as a rapid alternative to wet chemistry methods for the detection of adulteration of freeze- dried instant coffees. Spectra have been collected of pure coffees, and of samples adulterated with glucose, starch or chicory in the range 20-100 g kg-’. Two different FTIR sampling methods have been employed : diffuse reflectance, and attenuated total reflectance. Three different statistical treatments of the spectra were carried out. Firstly, the spectra were compressed by principal component analysis and a linear discriminant analysis performed. With this approach, a 98% successful classification rate was achieved. Secondly, a simultaneous partial least square regression was carried out for the content of three added carbohydrates (xylose, glucose and fructose) in order to assess the potential of FTIR spectroscopy for determining the carbohydrate profile of instant coffee. Lastly, the discrimination of pure from adulterated coffee was performed using an artificial neural network (ANN). A perfect rate of assignment was obtained. The generalization ability of the ANN was tested on an independent validation data set; again, 100% correct classifications were achieved.

Key words: coffee, adulteration, infrared, spectroscopy, principal component analysis, artificial neural networks.

1NTRODUCTION

The detection of fraudulent or accidental adulteration of foods is of interest to both food manufacturers and regulatory authorities. Suitable laboratory methods for this purpose are required by the food industry for moni- toring the quality of its products, as well as by govern- ment agencies wishing to check on the legal compliance of foods. The conlposition determines what a product may be called and at what price it may be sold; increases in coffee prices on the commodity market create the economic conditions in which adulteration becomes likely. clared plant material.

Coffee is defined in the U K Coffee and Coffee Pro- ducts Regulations (SI 1420) as ‘the dried seed of the coffee plant, whether or not such seed has been roasted

* To whom correspondence should be addressed.

J Sci Food Agric 0022-5142/96/$09.00 Q 1996 SCI. Printed in Great Britain

or ground, or both roasted and ground’. These regulations also define dried coffee extract, more commonly known as instant or soluble coffee, as ‘coffee extract in Powdery granular, flake Or cube solid form’. Instant coffee is more susceptible to adulteration than green coffee because of its powdered form. The Ministry of Agriculture, Fisheries and Food (MAFF) Working Party on Food Authenticity (WPFA) has carried out a national SUrVeillanCe exercise to investigate the authenticity of instant coffees (MAFF 1995). This survey, which was undertaken in 1993, found evidence that 15% Of the Samples contained varying amounts of unde-

There are different types of coffee adulteration. The

0 adulteration with coffee substitutes such as chicory, malt, figs, cereals, caramel, starch, malto- dextrins or glucose, as well as roasted or even

most commonly encountered are:

359

R Briandet, E K Kemsley, R H Wilson 360

a

unroasted coffee husks. (These ingredients may be added either before or after the extraction process, and in some cases even after drying); mixing of two species: addition of cheaper robusta to pure arabica coffee, with the product sold as pure arabica coffee; or mixing of expensive coffee beans from one growing region with cheap beans grown in another region.

Detection of instant coffee adulteration is not feasible by microscopic or other physical means traditionally used to identify impurities in green or roasted beans. Use- ful methods reported in the literature are generally based on chemical parameters. For example, the mineral or caffeine content (Newman 1981) has been used to char- acterise roasted or instant coffees. However, the levels of these constituents are greatly influenced by both growth and manufacturing conditions, and are too variable to provide a sensitive method for detecting adulteration. Other methods based on carbohydrate contents (Berger et a1 1991) have also been developed, in attempts to measure the percentage of chicory (Promayon et a1 1976) barley, figs (Kazi 1979) and caramel (Copestake et a1 1986) in commercial instant coffee. It has been found that high levels of total xylose, total glucose, free fructose and free mannitol are reliable indicators of adulteration (Blanc et a1 1989). Total xylose content has been recognised as a good tracer for fraudulent addition of coffee husks and skins; high levels of total glucose indi- cate adulteration with malto-dextrin or caramel; and high levels of free fructose may be indicative of adulteration with chicory. The current preferred method for determination of the carbohydrate profile is the technique of high performance anion-exchange chromato- graphy (HPAEC). In common with most wet chemistry methods, HPAEC is a time-consuming and expensive process: analysis of a single sample costs in the region of f300 and it is possible to analyse only six samples per day (MAFF 1995).

It has been reported that infrared spectroscopy is suitable for distinguishing arabica and robusta instant coffees (Kemsley et a1 1995; Briandet et a1 1996), and for determining the caffeine and chlorogenic acid contents of instant coffee mixtures (Fabian et a1 1994). In the present paper, infrared spectroscopy is explored as an alternative to existing methods for the detection of undeclared material in instant coffee. Spectra are collected using a Fourier transform infrared (FTIR) spectrometer, equipped with diffuse reflectance (DRIFT) and attenuated total reflectance (ATR) sampling stations.

Data processing was carried out using three different approaches:

(i) Linear discriminant analysis (LDA) (Massart et a! 1988) to distinguish between pure and adulterated samples was performed, preceded by

(ii)

(iii)

'data compression' of the spectra using principal component analysis (PCA). Simultaneous partial least squares (PLS) regression (Martens and Naes 1989) was carried out for the content of three added carbohydrates; xylose, glucose and fructose. This study aimed to establish the sensitivity of FTIR spectroscopy for making this kind of quantitative measure- ment and show that it has the potential for characterising instant coffees by their carbohydrate profile. An artificial neural network (ANN) was used to classify pure and adulterated instant coffee spectra. ANN are an unconventional form of data analysis that instead of being programmed to solve a problem in the traditional sense, learn about the data and then make generalisations when presented with new unseen data. For a full description of ANN see, for example, Long et a1 (1990) or Borggaard and Thodberg (1992).

EXPERIMENTAL

Instrumentation

All spectra were collected using a Monit-IR (Spectra- Tech, Applied Systems Inc) FTIR spectrometer, oper- ating in the region 800-4000 cm-l, equipped with a sealed, desiccated interferometer compartment and a deuterated triglycine sulphate detector. The instrument was equipped with two integral sampling stations: one was designed for the DRIFT sampling technique, the other for overhead ATR. The ATR station was fitted with a 45" zinc selenide crystal (1 1 reflections).

Spectral acquisition

All spectral measurements were made at 8 cm- resolution, with 64-interferograms

nominal co-added

before Fourier transformation. To obtain DRIFT spectra, each sample was ground for 5 min with a pestle and mortar, loaded into the DRIFT sample cup and the surface smoothed in a fixed direction with a spatula. Single-beam DRIFT spectra of each sample were trans- formed to Kubelka-Munk units using a background spectrum of ground potassium bromide. To obtain ATR spectra an aqueous solution of each sample was prepared (as detailed below). Spectral acquisition was initi- ated immediately upon application of the solution to the crystal; this protocol minimised the effect of settling of the small insoluble fraction on the crystal surface. Single-beam ATR spectra of each sample were trans- formed to absorbance units using a background spectrum of the clean dry crystal.

Adulteration detection in instant coffee 361

For the LDA and ANN analyses, all spectra were truncated to the region 900-1900 cm- '. To reduce the effect of irreproducible sample loading, a single point baseline correction at - 1900 cm-' was performed, fol- lowed by normalisation on the integrated spectral area (Kemsley et a1 1995). For the PLS calibration, ATR spectra only were collected. These were truncated to the region 970-1470 cm-' to focus on the carbohydrate absorbance bands.

Samples

The freeze-dried coffees used in our work were obtained from a range of different arabica and robusta beans, as well as blends of the two species. The supplier was able to guarantee their authenticity.

LDA was carried out with 129 spectra acquired with the DRIFT accessory and 90 spectra acquired with the ATR accessory. Of the 129 samples analysed by DRIFT, 41 were non- adulterated and 88 were adulterated in the range 20-100 g kg-' of glucose, starch or chicory. Of the 90 samples analysed with ATR, 44 were non-adulterated and 46 were adulterated in the range 50-100 g kg-' of glucose or chicory. For the DRIFT analysis, the adulterated samples were prepared by mixing together pure coffees and adulterants, and grinding these mixtures for 5 min with a pestle and mortar. In addition, approximately 1 g samples of a selection of these mixtures were dissolved in 3 ml of deionisecl water at 50°C and the solutions freeze-dried; this ensured thorough mixing of adulterant and coffee. For the ATR analysis, all samples were dissolved in deionised water at 50°C. To produce adulterated samples, solutions were prepared from mixtures of pure coffees and adulterants. The total solids concentration of all solutions was 0.33 g ml-'. For the carbohydrate calibration study, 30 mixtures of freeze-dried coffee with various propor-

tions of added glucose, xylose and fructose in the range 0-30 g kg-' were prepared as shown in Table 1. Solutions of each mixture were prepared as described for the ATR analysis above.

(iii) The ANN analysis was carried out with 146 spectra acquired using the DRIFT sampling technique. Samples were prepared as described for the DRIFT analysis above. Of the 146 samples, 58 were pure freeze-dried coffees and 88 freeze-dried coffees adulterated in the range 20-100 g kg-' of glucose, starch and chicory.

Data processing

PCA and LDA were carried out using Win-Discrim (EK Kemsley, Norwich, UK), a specialised package for spectral discriminant analysis.

PLS regressions were performed with Unscrambler I1 (Camo, Norway).

Classification with ANN was carried out using Neur- aldesk (Neural Computer Sciences, Southampton, UK).

RESULTS AND DISCUSSION

Linear discriminant analysis

The 'fingerprint' regions of typical spectra of pure and adulterated freeze-dried coffees acquired by the DRIFT and ATR sampling accessories are shown in Fig 1 and 2, respectively. A water spectrum has been subtracted from the ATR data shown here, for clarity only: water- subtracted data were not used in the subsequent data processing. With both sampling techniques, some differences can be identified between the pure and adulterated types, particularly in the regions 900-1 100 cm- ' and 1600--1800cm-'. However, the spectra are so complex that multivariate statistical techniques are needed to detect reliably the spectral contribution arising from non-coffee material.

Data processing comprising two distinct stages was carried out, starting with the 'data compression' step of

TABLE 1 Concentrations (g kg- ') of added xylose, glucose and fructose

Sample number I 2 3 4 5 6 7 8 9 10 I 1 I2 13 14 15

Xylose 0.0 20.7 6.8 18.1 22.1 0.0 25.4 20.0 15.4 18.8 9.4 0.0 23.1 4.0 20.6 Glucose 0.0 5.3 11.5 18.0 21.8 24.0 0.0 23.3 9.7 5.8 1.8 20.0 12.0 16.1 19.0 Fructose 0.0 4.0 7.6 24.1 0.0 26.2 19.5 37.4 27.0 12.3 5.5 0.0 3.5 3.9 20.1

~

Sample number 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Xylose 2.2 19.9 15.6 4.5 0.0 6.9 12.9 1.5 18.3 1.1 11.9 16.5 20.0 6.6 3.6 Glucose 3.7 12.3 13.4 1.7 0.0 16.5 9.2 1.2 19.5 14.3 8.9 12.5 0.0 7.7 5.3 Fructose 17.4 9.2 5.1 2.9 20.0 20.4 10.7 2.6 24.6 5.2 11.5 14.2 0.0 9.9 7.9

362 R Briandet, E K Kemsley, R H Wilson

2000 1800 1600 1400 1200 1000 800

Wavenumbers

Fig 1. Typical spectra of pure and adulterated (50 g kg-') freeze-dried coffees acquired with DRIFT sampling accessory.

PCA, which removes the redundancy in the original data set such that only the first few principal component (PC) scores are required to describe most of the information contained in all the original data. The first two PC scores of the DRIFT and ATR data sets are shown in Figs 3 and 4. It is immediately clear that there is partial clustering of the data according to the presence of adulterant in the samples. The spatial separation appears better with the PCA of ATR spectra, but this may be due to the differences in the samples used for each data set: in the DRIFT samples, the lowest concentration of adulterants is 20 g kg-', while it is 50 g kg-' in the ATR samples. There is a degree of overlap between the pure and adulterated coffees in both these plots. However, a two-dimensional plot does not wholly reflect the relative positions of the groups in PC space, and further PC dimensions may enhance their separation. Nevertheless, two-dimensional plots provide a useful visual aid for examining clustering in the data.

Next, LDA was performed. This uses the compressed data and known classifications to create a set of 'class means' and then re-classifies existing observations to the nearest class mean. The metric employed was the

coffee + chicotv h,

coffee + starch I ' I

J coffee+ glucose I"\

2000 1800 1600 1400 1200 1000 800

Wavenumbers

Fig 2. Typical spectra of pure and adulterated (50 g kg-') freeze-dried coffees acquired with ATR sampling accessory.

Mahalanobis D2 distance. An estimate of the likely success rate for the assignment of future unknown observations is obtained from the successful re- classification rate. This success rate depends in turn on the number of PC scores used to define the class means, as shown in Fig 5. As more PCs are used in the LDA so more relevant information is included, and the success rate rises. The rate of correct re-assignment reaches 98% for both sampling techniques with only four PC scores. Therefore substantially all the information needed to discriminate between the pure and adulterated instant coffees is comprised in the first four PC scores. We emphasise that this analysis has been carried out using a representative range of coffees and likely adulterants, and that the discrimination is successful even in the presence of compositional variations due to the different countries of origin, conditions of growth and species of the coffees and the quantity and nature of the adulterants. Although LDA is intended for prob- lems in which the classes have roughly equal spread (in mathematical terms, equivalent dispersion matrices) many workers have found that it is a robust technique and gives good results even when this condition is not met. Consequently, whilst we recognise that the classes

Adulteration detection in instant cofee

-3 ,a 10

363

3

E B x o U C cu

-3

-6

pure coffee

A coffee+glucose

0 coffee+starch

+ coffee+chicory

6 A

-8 -4 0 4 8 1 st PC score

Fig 3. Plot of the first vs second PC score of the DRIFT spectra.

pure coffee

A coffee+glucose

+ wffee+chicory

1u3 2r--- I

0

a

a

a

0

A A A

A A

A I x -3

-7 -2 3 8 -2

-1 2 1 st PC score

Fig 4. Plot of the first vs second PC score of the ATR spectra.

98%

I w

93% E' C

I I m

m 88% ti

.-

i 0

83%

78%

DRIFT

ATR

t-

1 2 3 4 5 6 7 8 9 10 Number of PC scores

Fig 5. Results of the classification vs the number of PC scores used in the discriminant analysis for both DRIFT and ATR

spectra.

defined in the present analysis may have unequal variability, the good results obtained clearly vindicate the use of LDA.

A potential danger of the use of the Mahalanobis D2 metric is that 'over-fitting' can occur if the number of observations does not sufficiently exceed the number of PC dimensions used. In the over-fit case, a 100% success rate is obtained at the model development stage, but the model may have no genuine predictive ability. However, a general 'rule of thumb' states that the over- fitting regime is entered only when the number of dimensions exceeds one-third the number of observations; we have found this rule to be upheld in other, similar analyses (for example, Lai et a1 1994) and thus conclude that the present analyses (using four PC dimensions and, respectively, 129 and 90 observations) are not over-fitted.

The chemical origin of the discrimination was examined through interpretation of the PCA loadings. PCA loadings represent independent sources of spectral variability present in the data set. Figures 6 and 7 compare the first three loadings of both sampling techniques with a selection of spectra of pure compounds. In both the ATR and DRIFT data sets, primary sources of variability are caffeine (major features in the region 1550-1750 cm-') and chlorogenic acid (major features in the region 1150-1300 cm- '). Similarities occur between the spectra of these pure compounds and loadings 1 and 3 for DRIFT spectra, loadings 1, 2 and 3 for ATR spectra. However, this variability is due not to the adulteration but to compositional differences between the coffee species: robusta coffee beans contain much more of these compounds than arabica coffee beans (Smith 1985). Glucose and starch explain a large part of loading 1 (in the region 1000 cm-') loading 2 (in the region 1000-1200 cm-') and loading 3 (in the region 1000-1200 cm- ') for the DRIFT analysis. The discrimination in the ATR data set is likely to be due to glucose


chlorogenic acid

caffeine A-

glucose 6 Loading 2

2000 1800 1600 1400 1200 1000 800

Wavenumbers

Fig 6. Comparison of the first three PC loadings of the DRIFT analysis and the spectra of pure compounds.

in loadings 2 and 3 (in the region 1000-1200 cm-I). The pure chicory spectrum is less characteristic than the glucose and starch spectra, and thus more difficult to compare with the loadings. The largest feature occurring in the first ATR loading in the region 1600- 1700 cm-' may be due to the relative difference in water content of the samples, and is unrelated to the presence of adulterant.

Carbohydrates profile

From consideration of the ATR spectra of the pure coffees, and of pure xylose, glucose and fructose solutions, the region 970-1470 cm-' was chosen for this study as it is the region in which carbohydrates exhibit the most intense features. PLS regression was used to obtain a calibration between the spectra acquired of the 30 mixtures (Fig 8) and the concentration of added xylose, glucose and fructose in each sample. Using internal cross-validation, optimum regression models were obtained using four PLS factors for fructose, five factors for glucose and six factors for the xylose. Figure 9 shows the internal cross-validation predictions for the

chlorogenic acid

caffeine A

chicory

2000 1800 1600 1400 1200 1000 800

Wavenumben

Fig 7. Comparison of the first three PC loadings of the ATR analysis and the spectra of pure compounds.

three carbohydrates. The standard errors of prediction (g kg - ', calculated from the internal cross-validation results) and the correlation coefficients were respectively 4.5 and 0.98 for glucose, 2.1 and 0.99 for xylose and 4.2 and 0-99 for fructose. We thus conclude that FTIR has the required sensitivity for determining the carbo-

1050 1350 1253 1150 1050 950

Wavenumben

Fig 8. ATR spectra of the 30 mixtures of coffee, xylose, glucose and fructose (with a water spectrum subtracted for

clarity).

Adulteration detecrion in instant cofSee 365

r.* - *

0 10 20 30 40

Actual added Carbohydrates (gkq-’)

Fig 9. Predicted vs actual added xylose, glucose and fructose in the 30 mixtures.

hydrate profile of freeze-dried coffees. Of course, to quantify the total (rather than added) carbohydrate contents requires further work, using fully characterised reference samples.

Artificial neural networks (ANN)

ANN have only recently begun to be used in the field of analytical chemistry. An ANN consists of ‘layers’ of data values (or ‘neurons’) interconnected by series of weight values (or ‘synapses’). In the present study, spectral data points are entered into the input layer and a

pure coffee

0 conee+glucose

A coffee+starch

- coffee+chicory

1 T-- I

I *

0 00

Adulterated I

- I

0 10 20 30 40 50 80 70 80 90 Sample number

Fig 10. ANN classification of the training set.

pure coffee

0 coffee+glucose

A coftee+starch

- wffee+chicory

. I ** ..

Adulterated 0

0 10 20 30 40 50

Sample number

Fig 11. ANN classification of the independent validation data set.

binary number representing the class designation (1 = pure, 0 = adulterated) is assigned to a single output value. The network architecture further com- prises a single ‘hidden’ layer of eight neurons between the input and output layers. The synapses are deter- mined in an iterative ‘learning’ step, in which the network is presented with a series of training samples for which the output values are known. Different algo- rithms can be .:sed to optimise the synapses. The most widely used algorithm is known as standard back- propagation. We used a variant of the latter called stochastic back-propagation which differs from standard back-propagation in the point at which synapse changes are made. With stochastic back-propagation the synapses are altered after presentation of each sample. Because the order in which the input spectra are presented may influence the learning they are presented in random order.

The generalisation ability of an ANN is its capacity for correctly predicting the output values for samples not used in the learning process. Both standard and stochastic back-propagation must be used with some care, since with both methods it is possible to over-train the ANN, such that it performs very well on the training samples, but has poor generalisation ability. We have guarded against over-training by ‘cross-validating’ during the learning process. Of the 146 spectra available for this study, 96 were designated training samples, and the remaining 50 reserved as independent validation samples. The 96 training spectra were divided into two


equal subsets. One of these subsets was used to train the network. The other was used a t regular intervals during the learning process to query the network; when the successful classification rate for these samples reached a maximum, before beginning to decline once more, the learning process was terminated.

With the A N N architecture described above, 100% of the training/cross-validation set were correctly classified by the network upon termination of learning. A representation of the A N N outputs is shown in Fig 10. The generalization ability of this network was tested on a completely independent validation data set : we emphasise that these samples were not involved in the training or cross-validation processes a t all. The same 100% success rate of classification was obtained (Fig ll), demonstrating the power of the ANN method as a n alternative to more conventional chemometric methods.

CONCLUSIONS

The results presented show that FTIR spectroscopy is a candidate method for detecting certain types of undeclared material in freeze-dried instant coffees. The three different statistical approaches yield complementary information. The PCA gives a visual representation of the position of samples in a low-dimensional space, and the LDA uses the relative positions of samples in this space to form a useful classification rule. Interpretation of the PCA loadings helps to give meaning to the direc- tions in the PC space. The PLS regressions for the added xylose, glucose and fructose contents show that FTIR spectroscopy has the required sensitivity for determining the carbohydrate profile of samples, although to develop this approach fully, a series of characterised reference samples are required. The A N N analysis offered a n improvement over the classification results obtained by LDA, for both training and test series of spectra. We believe it is a useful alternative to conventional chemometric methods and is likely to find increased popularity in the future.

ACKNOWLEDGEMENT

The authors would like to thank M r G Downey, of Teagasc, National Food Centre, Dublin, Ireland for supplying the samples used in this work. The BBSRC (E

K Kemsley, R H Wilson) and C O M E T T (R Briandet) are acknowledged for financial support.

REFERENCES

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Approaches to Adulteration Detection in Instant Coffees using Infrared Spectroscopy and Chemometrics