Chemometrics as an aid in food research and development

Computer Aided Innovation of New Materials H M. Doyama, J. Kihara, M. Tanaka and R. Yamamoto (Editors) © 1993 Elsevier Science Publishers B.V. All rights reserved.

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Chemometrics as an aid in food research and development Tetsuo Aishima Kikkoman Corporation, Research and Development Division 399 Noda, Noda, Chiba 278, Japan Chemometrics has been popular in food research and development due to its practical effectiveness for solving complicated problems. Nutrition, safety and preference are the major subjects in this field. Mutual relationships among constituents should be taken into account in every subject due to their intrinsic multivariate nature. In these two decades, the numbers of multivariate approaches applied for correlating instrumental data on food components to their sensory properties have been increasing. Recent topics of applications of pattern recognition including artificial neural networks for instrumental data on food components and the sequential simplex algorithm applied for optimizing mixing ratios of ingredients are reviewed by especially focused on flavor. 1. INHERENT COMPLEXITY OF FOOD Although major components of foods are water, proteins, carbohydrates and lipids as well-known, the key factors deciding sensory properties, i.e., flavor, are other numerous minor components. Thus food can be recognized as a set of various compounds in food science and technology. Among numerous compounds found in foods so far, volatile compounds and water-soluble low molecular compounds are responsible for food aromas and tastes, respectively. If instrumental analyses will be applied for food, the number of such minor components detectable amounts up to several hundred. Concerning aroma components, the number of whole compounds identified in foods so far has surpassed 6,000 [1]. Many of them have commonly been identified in different foods. In coffee, for example, more than 700 compounds have been identified as its aroma components [2]. However, no single compound or a group of compounds which can be reminiscent of the coffee aroma have not been found. Such "characterimpact compounds or key compounds" have been identified only in the limited number of fresh fruits or vegetables. In most foods especially in cooked ones, their aromas should be recognized as the results of integrated or interactive effects of many compounds. Flavor is defined as the food properties perceived by the senses located in the mouth when food is taken and the sum of the characteristics of the materials

which produce the sensation. Flavor can be decomposed into properties perceived by chemical senses such as aroma and taste and those perceived by physical senses such as texture, sound, and temperature. Color or appearance should be another important factor for deciding preference of foods. Thus flavor can be expressed as the following equation [3]. Flavor=/^aroma+/? 2 taste+^texture+0 4 sound+ ftcolor+jS6temperature ft, the coefficients or weights for sensory properties, are different from food to food. In some foods such as coffee or brandy, the aroma may be heavily weighted on the flavor but for some vegetables or snacks the sounds may be heavily weighted. Thus the flavor recognized as a set of sensory properties clearly suggests the multidimensionality of food besides multidimensionality in chemical components. 2.INSTRUMENTAL ANALYSIS As the first step for scientific understanding of flavor, food components should be identified and quantified. The purpose of food analysis in this context is to make a quantitative list for as many components as possible. On the other hand, the number of the target components may be limited

896 within a few compounds in most of other chemical analysis. Although application purposes are different according to features of instrumental analyses, three analytical methodologies such as chromatography, spectrometry and sensors can be applied for analysis of food components. Analytical data using such instruments are a vector for a single sample and a matrix for more than one samples. Thus data obtained from instrumental analysis of food are multivariate in nature. 2.1. Chromatography After introducing Chromatographie analysis into the investigation of food components in 50's, the numbers of compounds identified in individual foods have kept increasing. Especially capillary GC, HPLC and hyphenated instruments such as GC-MS, GC-IR and LC-MS have been accelerating such trend. GC analysis coupled with a semi-automatic purge-and-trap or dynamic headspace concentrator with cryogenic focusing has been used as a routine analytical methods for aroma components. Gas Chromatographie data of food volatiles have been widely used to verify effectiveness of pattern recognition and calibration methodologies [3]. 2.2.Spectrometry Since the coupling of a near-infrared reflectance (NIR) spectrometer with a personal computer (PC) in early 80's, NIR analysis has rapidly propagated into the food and agricultural fields [4]. NIR analysis are suitable for quality control at both production plants and harvesting fields besides in laboratories because of its convenience, durability and non-destructiveness of samples. Absorbance at 700 wavelengths for every sample can be obtained from one measurement in NIR analysis. Nowadays, an NIR spectrometer hooked up with a computer installed sophisticated calibration and pattern recognition software can be commercially available. Although application may be limited due to its high price and mechanical delicacy, *H and 13C NMR, another nondestructive spectrometry, have already shown great capability in discrimination and solving adulteration problems [5]. 2.3. Sensors Although only a single output value obtained from a measurement using a conventional sensor device such as a pH meter, the resulting output becomes a vector if different sensors are combined and

integrated into one device. Some attempts to discriminate aromas using a gas sensor array and subsequent pattern recognition have been reported [69] since late 80's. These new devices were designed in order to complement gas chromatography by their continuous aroma monitoring ability. 3.SUBJECTrVTTY IN SENSORY EVALUATION In food research and development, only the sensory evaluation using human senses can give the final decision for the quality assurance. In food industries, the quality consistency in products mainly depends on the experiences and keen intuition of experts. However, the sensory evaluation using human five senses as measuring instruments is a subjective judgement in nature. Although various multivariate analyses have been applied to sensory data since the first application of factor analysis to sensory data in 1951 [10], sensory evaluation implies limitations and defects derived from the inherent fluctuation and fuzziness in the human responses. Further, information provided by sensory evaluation is not easy to interpret as the term of chemical components. Until now, no instruments which can measure the aroma, taste or preference of food have been developed. All instruments applied for flavor analysis are merely measuring total ions or specific signals from compounds or elements, electric impedance or weight changes caused by aroma or taste compounds. The human nose and mouth can only detects the biological activity of a component, i.e., aroma and taste. However, so far, in spite of many efforts, the detecting mechanisms in the olfaction and taste have not been known. Therefore, a device mimicking olfaction or taste mechanism in the true sense has not been developed. If the recently discovered proteins expressed only in the olfactory epithelium are true odorant receptors, a true biomimic olfaction device may be created in not far future [11]. 4. OBJECTIVE EVALUATION OF FOODS As chemometrics is a discipline which uses mathematical and statistical methods in order to provide maximum chemical information, most methodologies in chemometrics seems to be efficient for handling food relating data. Consequently, pattern recognition and calibration utilizing various

897 multivariate analyses are now widely applied to instrumental and/or sensory data in both research laboratories and production plants. The purposes of such applications can be classified as follows: correlating sensory evaluation to a chemical component(s), certification of origins of raw materials and/or products, inspecting adulteration, and quality control and assurance. The first pioneering works in this field appeared in late 60' as collaborated works of computer experts with food scientists by using hand-made statistical software on main frame computers. However, rapid propagation of chemometric approach in the food area started in early 80's. At that time food researchers started using commercial software on personal computers (PC). Thus, the progress in this field principally owes for the advances in analytical instruments, computers and their software. In order to supplement or substitute for sensory evaluation by overcoming its defects and limitations, i.e., subjectivity and fuzziness, many efforts for correlating instrumental data to sensory data have been made since the first attempt in 1968 [12]. Almost every unsupervised and supervised pattern recognition and calibration technique has been applied to the data matrix obtained from instrumental analysis of food components and/or sensory evaluation. In addition to food scientists, many chemometricians have utilized foods as samples for verifying their new methodologies [3]. After many attempts, following three analytical procedures utilizing chemometric techniques to instrumental data have become routine methodologies for objective quality evaluation and certification. (l)Pattern recognition and calibration applied for capillary GC data of volatile components in order to discriminate, classify samples regarding aroma quality and certify authenticity of raw materials and/or products. (2)Pattern recognition and calibration applied for HPLC data of nonvolatiles in order to discriminate, classify samples regarding taste quality and certify authenticity of raw materials and/or products. (3)Calibration and pattern recognition applied for NIR data of whole constituents of food in order to predict contents of specific components and discriminate or classify raw materials and/or products based on their whole components.

5.FLAVOR DISCRIMINATION SENSOR ARRAY

BY

GAS

Although pattern recognition and calibration applied for GC data can objectively discriminate and evaluate aromas, the mammalian olfactory system can do it without separating mixtures into individual components. Responses from receptor cells at the mammalian olfactory system are nonspecific for particular compounds or a group of compounds. All sensors developed so far do not respond to a specific compound or a group of compounds either. However, if different gas sensors are integrated to make a sensor array in order to mimic a set of receptor cells, a function similar to the mammalian olfactory system can be expected. Semiconductor [79] and quartz resonator [6] gas sensors have been utilized for constructing such "artificial nose". Semiconductor gas sensors detect the changes in the electric impedance when reducing aroma compounds are adsorbed on the highly oxidized surface. Although sensor properties can be somewhat controlled by changing the temperatures or doping trace metals into oxidized metals, the response is nonspecific in nature. A quartz resonator covered by a lipid bilayer changes its resonance frequencies when flavor compounds are adsorbed on the lipid layer. This frequency change corresponds to the weight increase caused by adsorbed compounds. Thus the selectivity in a quartz resonator depends on the hydrophobicity of the bilayer. In this attempts, statistical and artificial neural network pattern recognition techniques were applied to the responses of gas sensor arrays to food volatiles for discriminating and classifying sample aromas. Six semiconductor TGS gas sensors made of tin oxide were installed in a flask to construct an "artificial nose". Aroma adsorbed on porous polymers were thermally desorbed and introduced into the flask. Water, methanol and ethanol can be removed from other adsorbed components using a trap packed with porous polymers such as Tenax TA. Coffee, liquors and essential oils were correctly classified according to their origins by cluster analysis based on the six dimensional data matrices. Linear discriminant functions using three or four sensors responses succeeded to discriminate five different liquors, instant and ground coffees, coffee cultivars and coffee beans at five different roasting levels [8,9]. Pattern recognition utilizing artificial

898 neural networks (ANN) also succeeded to classify and discriminate the responses according to their origins. 6.ARTIFICIAL NEURAL NETWORKS The ANN consists of multilayers of nodes which correspond to neurons in the brain. Especially, the three-layer ANN has been most widely used for pattern recognition and calibration. The three layers are called as an input layer, a hidden layer and an output layer, respectively. In most cases, the back-propagation algorithm has been used for the training or learning process in the chemometric applications. Through many iterations in the training process, the multilayer ANN can solve problems, for some of which statistical pattern recognition and calibration techniques cannot give answers. However, ANN analysis needs much longer time than conventional statistical methods do if a PC is used. Although the ANN seems very efficient methodology for pattern recognition and calibration, it lacks some abilities which statistical methodologies usually possess. The following points should be taken into account to obtain reliable results from ANN analysis [13]. (l)The ANN tends to overfit for the input data through many iterations in the training process. The overfitted ANN just seems to perfectly account for the input data but it cannot be applied for assigning or predicting other samples. (2)The ratio between numbers of samples and input nodes, the number of nodes in the hidden layer and the number of hidden layers affect the reliability of the results and the training efficiency. However, the existence of the optimum ratio or numbers are still controversial subjects. (3)No reliable standard for testing obtained results from ANN analysis is available because the ANN algorithms are not based on a certain statistical distribution. A part of sample set should be retained in order to utilize them for testing the reliability of the results, if sufficient number of samples are available. (4)Contribution of each variable to the obtained results is difficult to know due to the multilayered network structure. Since 1990, several attempts of applying ANN analysis for spectrometric and Chromatographie data

in order to compare the capability with statistical pattern recognition or calibration techniques have been reported [14,15], Here, ANN pattern recognition analysis was applied for gas sensor responses, GC profiles and NIR spectra. In every attempt, most samples were correctly assigned into their origin groups. In some cases, the training process took more than hours by a 32 bit IBM PC. However, if once ANN will be trained for pattern recognition or calibration, then assignment or prediction of unknown samples can be performed instantly. The ANN calibration was applied to GC and sensory data for predicting sensory scores. The precision in the predictions was better than those obtained from multiple regression and principal component regression analyses. The predictions of sensory scores for test samples were also satisfactory. In the view point of practical application of ANN for calibration and pattern recognition, capability of ANN seems very attractive and promising. Because statistical background is not required for operators to use ANN in spite of its high performance. Today, the price difference between a PC and workstation is still not ignored. Therefore limiting factor for applying ANN may lie in the length of time required for training step if only applicable computer is a PC. 7.0PTIMIZATION Food manufacturing consists of several basic treatments of raw materials or ingredients, i.e., mixing, crush, separation, concentration, dilution, filtration, pressing, drying, heating, cooling, fermentation and enzymatic reaction. All treatments need to have optimized their operating conditions by considering restraint conditions, i.e., costs, time, yields and physical durability of processing plants. Although most optimization problems are still solved by experiences or intuition of experts, recently experimental designs, response surface methodologies and simplex optimization have been successfully applied instead of the one-factor-at-a-time strategy which can only be applicable for systems consisted of independent factors [16]. Optimizing mixing ratio of ingredients or materials has been mainly depended on the experts skill. Simplex optimization showed versatility for this old problem in a model system and actual juice processing. In the model system, mixing ratio of several essential oils can be correctly estimated by

899 simplex algorithm using the similarity coefficient (S) between GC profiles as the objective function (O^S^l.O) [17]. In this strategy, GC profiles of essential oils were mixed according to the simplex algorithm and compared with that of the target mixture in the programs. Then, the mixing ratio was sequentially optimized for maximizing S using the MDS algorithm [18]. Some advantages utilizing simplex optimization over other methods were indicated. Differing from calibration method using regression analysis, the number of GC analysis needed was minimized because GC analyses needed were only target mixtures and possible essential oils. Further, negligible ratios were assigned to the essential oils when they had not been actually mixed. The same methodology had been applied to the formulation of strawberry juice by optimizing adding ratio of essential oils to the juice concentrate in order to regenerate the fresh strawberry juice flavor [18]. In this optimization, the GC profile of the fresh juice was used as the target mixture. Finally, sensory evaluation confirmed that no statistical difference exited between the aromas in fresh strawberry juice and the reconstituted juices. 8.CONCLUSION Chemometric approach including the ANN for food research and development seems promising. Many flavor researchers have already well recognized the great advantage of multivariate approach to the flavor relating problems over univariate or bivariate methods. Consequently, pattern recognition and multivariate calibration have become routine methodologies for GC, HPLC and NIR data analysis. However, other chemometric methodologies have been rarely used in food relating fields due to the unawareness of other efficient methodologies by researchers. Appropriate education in the college curriculum, standardization of procedures, and development of more user-friendly computers and software are needed for propagating chemometrics into food research and development. In this context producers and distributors of analytical instrumental play the essential roles. Some instruments have been already coupled with PCs installing sophisticated software. However, most computers hooked up with analytical instruments are only used for data acquisition in spite of their high capability and excess

excess memory area. Analytical instruments hooked up with efficient software will greatly help to accelerate propagation of chemometrics into the food area.

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