Elsevier

Talanta

Volume 148, 1 February 2016, Pages 54-61
Talanta

Prediction of banana quality indices from color features using support vector regression

https://doi.org/10.1016/j.talanta.2015.10.073Get rights and content

Highlights

  • Firmness, TSS, TA and pH of the banana during shelf-life were measured.

  • Correlation between color features and quality indices were investigated.

  • SVR–rbf was chosen as the best model for prediction of quality indices.

Abstract

Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acidity and firmness as the output. Experimental results provided an improvement in predictive accuracy as compared with those obtained by using artificial neural network (ANN).

Introduction

Banana is one of the most popular fruits in the world and a main one in international trade [1]. In 2011, bananas (Musa spp.) were grown in 10.6 million ha with an average fruit yield of 13.6 t ha−1 [2], being Cavendish the most widely banana cultivar. Suitable temperature, humidity, time and air flow are all needed for ripening of bananas and by using controlled ethylene gas, the ripening treatment of banana could be artificially made [3]. During banana ripening period, the peel color changes, the flavor evolves and the pulp softens. Banana peel color changes from green to yellow, with brown spots appearing on the yellow color at the end of shelf-life; being the synthesis of few pigments the reason of this change in peel color [4]. So, on-line quality control of banana during ripening treatment is quite important to keep a firm pulp texture, good color and flavor and also to prevent from bruise [5]. This makes monitoring of quality parameters in the orchard, package and delivery points necessary in producing acceptable bananas for the customer [3].

Most of the fruit quality measurement methods are destructive such as pulp to peel ratio determination and fruit firmness, which are mainly based on rheological properties [6]. Also, these methods do not sufficiently monitor the quality of banana fruits during ripening period [5], being employed just a small set of samples as representative of the whole because of difficulties of destructively analyzing every unit of fruit [3].

The development of instrumental non-destructive techniques could be highly appropriate to increase the number of fruit pieces which can be analyzed, to repeat several times the analysis on the same sample at a given time or during its physiological evolution, and to access to real-time information [7].

Several soft and non-destructive techniques that can be applied for quality measurement include proton transfer reaction mass spectrometry (PTR-MS) [8], ultrasound [9], magnetic resonance imaging (MRI) [10], time-resolved reflectance spectroscopy [11], X-ray and computed tomography (CT) [12], laser-induced fluorescence spectroscopy (LIFS) [13], Fourier transform infrared (FTIR) [14], sonic technique [15], near infrared spectroscopy (NIRS) [16], sound velocity [17], optical chlorophyll sensing system [18], capacitance technique [5] and electronic nose systems [19]. Nevertheless, most of the aforementioned analytical techniques are far from reaching a practical application and, sometimes, are time-consuming, expensive, difficult to be implemented and require to be made by trained personnel [7].

Near-infrared (NIR) spectroscopy has been used to correlate firmness and soluble solid content (SSC) in Cavendish bananas at different stages of ripeness [3], [20]. On the other hand, Baiano et al. [7] proposed the use of hyperspectral imaging technique for prediction of some physico-chemical parameters of table grapes. Concerning banana shelf life, studies made by Wang et al. [21], Bora et al. [22] and Mendoza et al. [23] evaluated the ripening stage of bananas from their color but without making any quantitative study about their quality indices. However there is no precedent, in our own knowledge, about the use of external color of bananas to evaluate their quality.

At first instance, fruits quality is evaluated by looking at their color, gloss, size and, secondly, by texture, total soluble solids (TSS) content and acidity. These parameters may provide important information to the consumer in the choice of food supply. A special emphasis should be placed on quality attribution in trading. The ripening indices traditionally used to evaluate changes in skin color, softening, titratable acidity, soluble solids concentration and volatile compounds [7], [24]. On the other hand, the application of sensors; such as optical, chemical, and tactile ones, provides a high correlation with the human senses. Different techniques have been reported to determine various quality parameters of fruits. However, there is currently no single or combination of techniques and computational methods available to quantify the overall quality of foods [25].

Color can be correlated with other quality attributes such as sensory, nutritional and visual or non-visual defects and helps to control them directly [25], [26]. Color is considered a basic physical property of agro-food products. In fact, color plays an important role in the evaluation of external quality in food industries and food engineering research [7], [27]. The role of color is important to evaluate food quality, but the use of color parameters to predict specific quality indices of foods has been less explored and represents an opportunity among researchers.

Industrial food product quality was monitored and controlled by an on-line imaging system [25], [28]. However, there are only few studies available on modeling kinetics of green–yellow transformations in fruits and vegetables. Engineers need quantitative models to develop and improve processes [29]. Therefore, current investigation was conducted to study changes in color features of banana during shelf-life and to evaluate the use of image analysis technique as a rapid and nondestructive alternative for the accurate prediction of quality indices.

On the other hand, the scientific literature shows the valuable application of artificial intelligence in food research field [30], [31], [32], [33], [34] and radial basis function-based support vector regression has been employed to process color data of bananas at different shelf-life stages.

Section snippets

Experimental material

Banana fruits (Cavendish variety) imported from the Philippines were used in this research. The banana fruits were stored at 14 °C during transportation. Then, the fruits were stored in an airtight warehouse. Bananas' ripening was completed in 4 days. In the first day, fruits were stored at 20 °C, and in the second day, ethylene gas was injected. In the third day, ethylene was removed and temperature decreased to 18 °C and finally temperature was decreased so that it achieved 11 °C in the fourth

Color features

The changes of banana color features during the shelf-life period are shown in Fig. 2. L* was reduced during the shelf-life period. However, the a* color feature has an upward trend over the period which means decline in greenness of fruit skin. Generally small changes were observed in the b* values during that period. A gradual increase was seen in b* value till 5th day of shelf-life (end of ripening period), which stabilized after that, addressing an increase in yellow color of banana peel.

Correlation between color feature values and quality indices.

The changes of quality indices relating to the color parameters which provided the best correlation with each of them are shown in Fig. 4.

a*, b* and h were the best estimators of TSS, acidity parameters and firmness respectively. As the a* color component ranges from green to red, it can be concluded from Fig. 4(b) that the greener the banana the lower the TSS value. On the other word, unripe bananas have lower TSS contents than ripe ones. The higher b* values introduce the increase of the

Conclusions

Support vector regression (SVR) of color parameters provides a useful model for prediction of the quality indices of bananas. The use of a radial basis function (SVR–rbf) permitted to estimate quality indices, component concentrations, chemical and physical properties from color features of banana during shelf-life. The SVR approache enhanced the outcomes provided by ANN and create smaller estimation errors in comparison with ANN. Moreover, the SVR technique can be applied in the fields related

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