Elsevier Editorial System(tm) for Food Chemistry Manuscript Draft Manuscript Number: Title: MONITORING METHODS AND PREDICTIVE MODELS FOR WATER STATUS IN JONATHAN APPLES Article Type: Special Issue: 7th Water in Food Keywords: water forms, Jonathan apple, drying, imagistic analysis, neural network Corresponding Author: Dr Lucia Carmen TRINCĂ, PhD Corresponding Author's Institution: "Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine, Faculty of Horticulture, First Author: Lucia Carmen TRINCĂ, PhD Order of Authors: Lucia Carmen TRINCĂ, PhD; Adina-Mirela CĂPRARU, PhD; Dragoş AROTĂRIŢEI, PhD; Irina VOLF, p; Ciprian CHIRUŢĂ, PhD Abstract: In this paper, data on status of water forms (free water, physico-chemically bound water) in Jonathan apples (pieced, grinded apples and apple juice) studied for a period of 20 days are presented. Investigations included physical and chemical analysis methods based on weighing, oven drying, freeze drying (lyophilization) and FTIR spectroscopy as well as texture parameters imaging analysis. Statistical calculation showed interdependencies between investigated forms of water. Correlations between free water loss and texture parameters imaging analysis variations were the basis for neural networks predictive models. Measure of approximation performances for the proposed prediction applications generated a maximum absolute error (max_abs_error) of 4.57 * 10-7 for neural network and 3.28 * 10-7 for optimized neural network.
Cover Letter
The "Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine of Iasi The Faculty of Horticulture Science Departament Str. Aleea M. Sadoveanu, no. 3, zip: 700490, Iasi, Romania Phone: + 04 0232 407547 Fax: + 04 0232 260650
November, 2012
Cover Letter
: Enclosed please find an electronic copy of a manuscript entitled “MONITORING METHODS AND PREDICTIVE MODELS FOR WATER STATUS IN JONATHAN APPLES ” which we wish to submit for publication in Food Chemistry. The manuscript is not concurrently under consideration for publication in another journal. All authors were involved in the work leading to the submission of the paper and have read the manuscript before submission. We believe the manuscript is complete and prepared according to the Guide for Authors. However, we would be glad to make any required corrections . We appreciate the opportunity to submit this paper for your consideration.
Sincerely, Lucia Carmen TRINCĂ, Ph.D. "Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine, Faculty of Horticulture, Iasi, Romania
*Highlights (for review)
Food Chemistry Imagistic Analysis Predictive aproaches based on neural networks
*Manuscript Click here to view linked References
MONITORING METHODS AND PREDICTIVE MODELS FOR WATER STATUS IN JONATHAN APPLES
Lucia Carmen TRINCĂ * - "Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine, Faculty of Horticulture, Str. Aleea M. Sadoveanu, no. 3, 700490, Iasi, Romania,
[email protected] Adina-Mirela CĂPRARU - "Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine, Faculty of Horticulture, Str. Aleea M. Sadoveanu, no. 3, 700490, Iasi, Romania,
[email protected] Dragoş AROTĂRIŢEI - “Grigore T. Popa” University of Medicine and Pharmacy, Faculty of Medical Bioengineering, Iasi, Romania, Str.Universităţii no.16, 700115, Iasi, Romania,
[email protected] Irina VOLF - “Gheorghe Asachi” Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection, 73 Prof. dr. Doc. Dimitrie Mangeron Street, 700050, Iasi, Romania,
[email protected] Ciprian CHIRUŢĂ - "Ion Ionescu de la Brad" University of Agricultural Sciences and Veterinary Medicine, Faculty of Horticulture, Str. Aleea M. Sadoveanu, no. 3, 700490, Iasi, Romania,
[email protected] * Corresponding author
1. INTRODUCTION Currently, the scientific literature deals with the characterization of water forms from food substrates at a general concept level (Baucour and Daudin, 2000, ElSayd and Makawy, 2010) with interests limited by the area of specialization approached (physical, chemical, biochemical, physiological or technological). Between water from food substrate and water from atmosphere there is a permanent
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change until the equilibrium moisture is reached. Depending on availability and biological activity, water from food substrates may exist in the forms of free water and bound water (Schmidt, 2007). Free water is available for chemical interactions, allows the development of metabolic processes, stimulates microorganisms development, being-not linked by the molecules of other biochemical compounds in food substrate. Basically it is considered that the free water appears as a real solution which is extracted by pressing or drying the product (Keshani et al, 2010, Derossi et al., 2007). Bound water is retained in the food matrix substrate through interactions with functional groups of biochemical compounds (salts, carbohydrates, lipids, proteins, vitamins) and exhibits limited biological activity, freezing temperature less than - 40 0C and boiling temperature higher than 100 0C. Depending on the nature of bonds and binding mode, food substrates may contain water bound physically, physico-chemically and chemically. Physically bound water is specific for hygroscopic substrates being retained by mechanical forces that refer to capillary forces in the case of porous substrates, or surface forces in the case of non-porous substrates (Derossi et al., 2007). Physico-chemically bound water is the most common form of bound water in food substrates (physico-chemical bonds are not formed in a ratio strictly determined (being the result of several types of forces) and can be split either by heat treatment (at food substrate specific temperatures), or by specific chemical interactions. Removal of physic-chemically bound water practically does not lead to complete degradation of food substrates. Chemically bound water looses mobility, chemical and biological activity and can not be removed from food substrates without causing degradation. Ionic and molecular bonds
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(chemically bound water features) are formed on strictly stoichiometric ratios and form strong bonds that can be destroyed by chemical reactions or by calcination (Derossi et al., 2007). If quantitative determination of bound water by Karl Fischer traditional method has generated controversy over the years (Bradley, 2010), recently there have been developed new techniques for its qualitative and quantitative analysis such as FTIR spectroscopy (Rohman et al., 2009, Vardin et al., 2008, van de Voort et al., 2004, 2003) NIR (Osborne, 2006), NMR and calorimetry (Goni et al., 2007). The ratio free water / bound water can be an useful indicator for assessing the development of physiological processes specific to organs (Lewis et al., 2010) or to characterize the metabolic state of an living organism, either vegetal (Zude et al., 2006) or animal (Powers et al., 2009). Analytical methods with respect to determination of water forms are quite limited, as well as the investigation techniques. In recent years research has evolved through association of classical analysis methods with those non-invasive, which are based primarily on imagistic analysis (Lu, 2004, 2003, Peng and Lu, 2007, 2006, Qing et al., 2008). The last refer to analysis of the shape or texture that, once were interpreted, statistically allowed the establishment of predictive models (Nguyen et al., 2006, Sanchez, et al., 2003, Saranwong et al., 2004, Schaare and Fraser, 2000). This paper aims first assessing free and bound water ( physically and chemically bound water) in Jonathan apples (pieced, grinded and juice) through various methods of determination (oven drying and freeze drying) and to perform statistical correlations for the experimental data achieved by ratio of variance analysis. Second, development of a predictive model based on neural networks for the parameters measured by imagistic processing is foreseen.
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2. MATERIAL AND METHODS 2.1. Materials In early November 2011, normal, healthy looking Jonathan apples were purchased from town market. Their initial weight ranged from 91-103 g. Periodic determinations accomplished for monitoring of the different forms of water were performed on 60 apples (considering the lot of 5 apples stored under the same conditions of temperature and humidity) by carrying out five determinations for each analysis performed. Studies were performed on samples of pieced apple fruits (peeled apples were chopped with a special cut device to 2 cm each), grinded apples (obtained by grinding apple pieces) and apple juice (obtained by pressing apple pieces). A balance type RADWAG AS 220 / C / 2 with accuracy of 10 – 4 g was used for weighing, a SLW 115 ECO drying oven was used for drying apples at temperature of 900 C and a Freeze Dryer ALPHA type 1 - 4 LD was used for lyophilization. An AF-S DX Zoom-Nikkon ED 18-70mm, f / 3.5-4.5 GIF was used in order to achieve image processing, while for ensuring the same calibration, photos were taken by placing apples in a box (aiming to keep the same distance objective – image). 2.2. Methods for chemical analysis and statistical processing 2.2.1. Free water loss determination Regular weighing (at every 5 days) of apples analyzed was performed for 20 days, the difference in weight being attributed to free water or loss moisture content percentage (LMC,%), according to equation.1: LMC ,% = 100 x (mi- mf) / mi where mi = initial mass of food substrate, mf = final mass of food substrate.
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(1)
2.2.2. Drying methods 2.2.2.1. Oven drying 5 grams of apples sample (pieced / grinded or apple juice) were subjected to drying at 900C until a constant mass ≤ 10-2 g was reached. Water (moisture) content (MC, %) was determined by equation 2 MC, % = 100 x (m- m1) / m2
(2)
where m = mass of the sample before drying (g), m1 = mass of the sample after drying, m2 = mass of the analyzed sample. 2.2.2.2. Freeze drying Lyophilization is a cold dehydration process: water freezes faster than other ingredients and is removed as ice without changing the structure of the food substrate. Lyophilization was carried out in a Freeze Dryer ALPHA type 1 - 4 LD plus. 5 grams of apples samples (pieced, grinded or apple juice) were subjected to freeze drying at temperature of - 50 - 60 0C, and pressure ranging from 0.02 to 0.03 mbar until a constant mass was reached. Water content was determined using the equation 2. 2.2.3. Spectral analysis methods 2.2.3.1. FTIR Spectroscopy FTIR spectroscopy shows functional changes occurring in the analyzed structure compared to the control substrate. FTIR spectra were recorded in KBr pellet using a spectrometer FTS EXCALIBUR DIGILAB-2000, equipped with a device for heating samples. Working parameters were: spectral range 4000 - 400 cm-1, resolution 4 cm-1 and the number of scans equal to 24.
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2.2.4. Statistical processing methods 2.2.4.1. Ratio of variance analysis, Pearson correlation coefficient and coefficient of determination Correlation coefficients (DeVore and Berk, 2004) for apple samples analyzed on days 5 and 20 for free water and physico-chemically bound water (determined by oven drying or freeze drying) of apple samples (pieced, grinded and apple juice) were identified through 2012 The MathWorks, Inc. MATLAB function (http://www.mathworks.com/). For statistical analysis of the recorded differences, the ratio of variances analysis (χ) method was applied, whereas Pearson correlation coefficient and coefficient of determination (by considering the case of α = 0.05 as statistically significant) have been calculated. 2.3. Imagistic analysis method An important method in the analysis and imagistic processing takes into account the texture characteristics for which, one of the simplest approaches is to utilize statistical methods using the histogram of the gray levels of an image or of a region of that image (Gonzalez and Woods, 2007). Texture analysis based on histogram only does not provide information on the relative position of the pixels, one against another. This fact led to the association of pixel intensity distribution (gray levels) with their position relative to other pixels that have the same value or close value. In this respect, the most commonly used descriptors are: maximum probability, element difference moment of order k, inverse element difference moment of order k, uniformity and entropy. Finally the following descriptors: average intensity (3), smooth (4), third order moment (5), uniformity (6) and entropy (7) have been used: L 1
I zi p( zi ) i 0
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(3)
S = 1-1/(1+σ2)
(4)
L 1
3 ( zi I )3 p( zi )
(5)
i 0
L 1
U p 2 ( zi )
(6)
i 0
L 1
e p( zi ) log 2 p( zi )
(7)
i 0
Density index (DI) was another investigated parameter. This is related to the apple volume that was approximated as being a sphere of radius R (≈ DI). In this case, the approximate R value given by initially image (Figure 1) pre-processing was used. The pre-processing stage involved segmentation (Figure 2), filling-in of holes, approximation of circle parameters, extraction of circle parameters and then, extraction of a rectangular area form for the apple (Figure 3), considered as a circle, for performing the texture analysis (the extracted area was identically localized and exhibited the same size for all the apples investigated). Practically, the image was segmented; the apple profile was extracted and approximated as being a circle with center C (xc, yc) and radius R. The approximation was performed using method of least squares LMS (as seen in Figure 2A) and contour points (as seen in Figure 2B). 2.4. Neural networks predictive methods Artificial neural networks are approximations of biological neural networks, while artificial neuron is an approximated mathematical model, which schematizes the functionality of biological neuron (Haykrin, 1998). Artificial neural networks are structural models interconnecting artificial neurons or nodes (Figure 4). Basically, implementation of approximation of the function YLMC = f (I, S, μ3, U, E, DI) was achieved based on neural networks. The data set was divided into a training set (where the
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neural network "learned" function with the desired accuracy) and the test set (which include values that were not used in training stage and that check the approximation given by the network, related to interpolations and extrapolations). Maximum absolute error was calculated according to the equation Eabs max | y i yˆi |
(8)
where yi is the resulted y, whereas ŷi is the estimated y.
3. RESULTS AND DISCUSSION 3.1. Methods for chemical analysis The dynamics of free water loss (LMC %) for a set of 5 apples samples is presented in Figure 5. One may observe from Figure 5, a linear increase of free water variation, which show a loss of 1.42 % for the period of 0-5 days, 1.45 % for the period of 5-10 days, 0.79 % for the period of 10-15 days and 1.69 % for the period 15 -20 days. Vesali and colab. (2011) reported for LMC, (%) determined experimentally on Golden Delicious apples variations between: 3.210.4 % (after 8 days), 9.1-23.7 % (after 16 days) and 15.5-31.4 % (after 24 days). These significant differences (Menges and Ertekin, 2006) can be explained by the particularities of the two varieties: Golden Delicious apples have many inner spaces (inner hollous) that allow the intensive removing of water (Rahman, 2006, Veraverbeke et al., 2003). By contrast, Jonathan apples can be stored for a long time without significant loss of water content. We considered LMC, (%) as beeing substrate free water wich result by biochemical and normal physiological processes (evaporation, transpiration, respiration)
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For a better interpretation of results, both statistical analysis of ratio of variances, and assessment of coefficient of determination (for day 20 against day 5) for free water have been used. Free water loss was higher on day 20 compared to day 5, the difference recorded being statistically significant (p 0.05) for grinded apples and apple juice, the same sense of variation being recorded for physico-chemically bound water determined by lyophilization. The significant degree of determination of values between the final state (day 20) compared to initial state (day 5) supports a linear dependence (Figure 6) for the direction of variation of the physic-chemically bound water content determined by lyophilization (r2 = 0.9183) and oven drying in the cases of apple juice (r2 = 0.6984) and grinded apples (r2 = 0.5771). On day 5, the physico-chemically bound water content for pieced apples varied significantly (p
