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Home / Colour Measurement and Analysis in Fresh and Processed Foods a Review

Colour Measurement and Analysis in Fresh and Processed Foods a Review

Introduction

The colour of foods is a meaningful characteristic since it is one of the first characteristics to be evaluated by the consumers, closely associated with the food quality.[one,two] In general, it is a complex awareness which depends, among several factors, on the consumer'south perception, the chemical composition of the food, and the incident light. All those factors indicate that the color is not only an intrinsic property of the sample simply likewise it is influenced past the environs.[2] In food engineering, the colour is commonly expressed in the CIELAB or CIE 1976 L*a*b* colour space,[iii] where 50* is the lightness, a* is the redness (from green to red), and b* is the yellowness (from blue to yellow). This is often adamant using colorimeters,[2] which measure the reflected light from a surface under standard lighting weather. These measurements can be easily performed for opaque solid foods, where almost of the light is reflected by the food surface.On the other paw, the colour of transparent or translucent foods, like beverages, is much more complex to define and mensurate. Aside from the influence of subjectivity on the consumer perception, the colour of translucent foods is greatly affected past the liquid's depth and the scene background. Up to the present, the colour of translucent liquids is usually determined using spectrophotometers, which measure the absorbance or transmittance spectrum, from which CIELAB color space data can be obtained.[4] These devices use cells of unlike depths, and the event of this variable in the colour value deserves to be studied. In this sense, Joubert[5] measured the colour of rooibos tea with cells of different depths using a spectrophotometer. Huertas et al.[6] measured the colour of wine samples with dissimilar thicknesses using a spectroradiometer and a spectrophotometer. González-Miret Martín et al.[seven] employed a spectroradiometer to measure colour of wines at different depths. Hernández et al.[8] measured the vino colour at the center and the rim of a normalised vino sampler using a spectroradiometer. Gómez-Robledo et al.[9] measured the color of virgin olive oil with cells of different thickness using a spectrophotometer and with cells of different diameters using a spectroradiometer. Carvalho et al.[ten] measured CIELAB colour of wines during ageing using a spectrophotometer.

On the other hand, the utilize of digital images to determine the colour of foods has been encouraged in the past few years,[two] mainly for the surface colour of solid opaque foods. To measure out color from digital images, a computer vision system (CVS), consisting of a digital camera, an image acquisition ambient with controlled illumination and information processing software, is required.[11] In these systems, one central step is the conversion of the Cerise, Green, and Blue (RGB) colour values obtained from digital cameras to the CIELAB color space. However, just few works have been reported using this methodology regarding the color of translucent liquid foods. González-Miret Martín et al.[vii] measured the color of 4 commercial wines; Fernández-Vázquez et al.[12] measured the color of orange juice samples and discriminated between samples based on the measured colour and a trained sensory console; both works used a commercial colour organisation, DigiEye (VeriVide, UK). Mendoza et al.[xiii] explored the feasibility of a machine vision technique for predicting the quality of commercial canned beans, using colour and textural features extracted from drained beans and alkali images. Hence, the objective of this piece of work is to develop a simple methodology to obtain the feature colour of translucent liquid foods from digital images. With this aim, a measurement cell with variable depth was congenital, thus allowing the evaluation of depth influence on the colour in a single paradigm. The digital images were processed to obtain L*, a*, and b* values from RGB information, using a standard colour chart and an empirical color space conversion model. A software bundle was developed in MATLAB, equally a graphical user interface, to process the images.

Textile and methods

Measurement cell and characteristic color

Various measurement cells or liquid container prototypes were designed, congenital, and tested. Finally, a transparent acrylic cell was employed, with its bottom painted in lite grey (Fig. 1). The jail cell has 8 mm width, 104 mm length, and a tilted floor, and so that its depth varies from 0 to 85 mm. To completely fill the jail cell, 35.4 cm3 of liquid are necessary.

Figure i. Flick of the developed acrylic cell.

Both liquid depth and background affect colour measurement; therefore, it is necessary to consider them in order to provide a suitable description of the colour. As it was mentioned, the color of liquids is defined equally the colour at space depth. Co-ordinate to the Beer-Lambert police,[14] an exponential function is proposed to represent the colour variation with depth (Eq. 1): (one)

where C refers to each color parameter, L*, a*, or b*, respectively. The subscript '0' refers to the initial value (z = 0), β is related to the light absorption on the sample, and z is the liquid depth. As the cell depth increases (z → ∞), each color parameter approaches a constant one, L*∞, a*∞ , and b*∞ . Then, these values are defined equally the characteristic colour of the sample. It is noteworthy that more general and detailed theories of light propagation on turbid media, as the Kubelka-Munk theory, are available,[fifteen] simply nevertheless, a simple and empirical approach was chosen according to the aim of this work.

Image acquisition and processing

To obtain the digital images and then the colour of the samples, a CVS is required, which consists of an image acquisition sleeping accommodation, a digital camera, and image processing software. In this work, the image conquering was performed in a laboratory room with fluorescent lights, using a Samsung ST60 digital camera (automatic program, flash off, 3000 × 4000 pixels, ISO 100, white balance: white fluorescent).

Both the image processing and subsequent calculations were performed in a software package developed using MATLAB® (The Mathworks Inc., Natick, Mass., U.s.), as a graphics user interface (GUI) adapted from.[xvi] This software, developed to measure out the colour of solid foods, was previously tested and validated.[17,18] Since the digital camera acquires images in the RGB color space, a conversion to the CIELAB colour space must exist practical. The direct conversion between these ii colour spaces[19,20] is suitable when standard illumination conditions are used. On the contrary, when the lighting is non standard, an empirical conversion must exist performed, as it is stated in previous works regarding the measurement of the colour of the solid foods.[21–26] These empirical models must be fitted by a calibration procedure using samples with known colour values. In this work, the post-obit empirical conversion model was used, with linear and quadratic relations, as well as interactions between the RGB values: (2)

The parameters αi of this empirical conversion model were obtained using a 10-Rite ColorChecker nautical chart (X-Rite Inc., Chiliad Rapids, Michigan, USA), which is a standard reference used in several studies.[23,25,27] It has 24 patches with dissimilar colours, with known L*a*b* values;[28] in this work, D65 illuminant was considered. The error of the fitting procedure was assessed using the average total color deviation between L*, a*, and b* colour values of the ColorChecker and the predicted (Eq. ii) ones: (iii)

where subscripts F and R refer to fitted and reference values, respectively, and n refers to the number of ColorChecker patches (n = 24). The processing steps are summarised as follows:

  1. Obtain an epitome of the cell filled with the sample.

  2. Calibrate the colour infinite conversion model (Eq. 2).

  3. Select an initial and a final point in the liquid cell and ascertain the number of intermediate points to use (see Fig. 2).

  4. Obtain RGB values for each point. To diminish eventual noise, a small patch of several pixels was employed instead of using a unmarried point (see Fig. 2).

  5. Catechumen the RGB values to L*a*b* values for all the selected points, using Eq. (2).

  6. Calculate the depth z for the selected points.

  7. Finally, fit L*, a*, and b* values versus z (Eq. ane) to obtain C∞, C0 , and β.

Figure 2. Image of the apple juice sample opened in the GUI. The 20 patches selected to measure out colour versus depth are shown; the initial and final patches are indicated. Predicted RGB colours of these patches are likewise depicted.

The steps ii to vii are implemented in the developed software. The plumbing fixtures performance of Eq. (1) was too expressed using the average total colour difference betwixt measured and fitted color values, for all depths, similarly to Eq. (3).

Samples and experimental measurements

Several fruit juices (grape, pear, multi-fruit, apple), wines (white, rosé, red), lager beer, and energy drinks of different colours (grapefruit, blue), purchased at a local market in La Plata (Argentina), were employed to test the proposed methodology. Also, selected liquid cleaners and deodorants of diverse colours were used; these samples were included since they are constructed and present a more stable colour through time. All the samples were poured carefully in the jail cell, avoiding bubble formation; the beer sample was allowed to settle a few minutes until the bubbling disappeared. The photographic camera was placed 25 cm above the sample, vertically aligned with the cell, and a unmarried photograph was acquired for each measurement.

The L*a*b* colour of the whole set of samples was also estimated from spectral transmittance data[29,30] in guild to compare the operation of our methodology with that of the traditional method. For this, D65 standard illuminant and 2° standard observer were used, measuring between 380 nm and 780 nm at 1 nm interval (BECKMAN DU 650 Spectrophotometer, Brea, California, U.s.a.). A plastic prison cell of 1 cm depth was employed, using distilled water equally reference. The Hue angle (Eq. iv) and Chroma (Eq. v) were calculated from the L*a*b* values obtained by both approaches: (iv) (five)

The power of the developed methodology to detect concentration differences was determined by measuring the colour of ii samples (blueish energy drink and multi-fruit juice) diluted in distilled h2o. The final concentration of the diluted samples was 0, 5, 10, xx, 40, 60, and 80% (measured as sample volume/total volume). Boosted measurements were performed to test the influence of the background on the characteristic color. For this purpose, the cell floor was covered with plastic sheets of different colours (yellow, green, and black), and digital images with each background were acquired for a particular sample (blueish free energy drink). To analyse the influence of the background, a hypothesis exam on the equality of ways was done.[31]

Results and give-and-take

Scale of the conversion model

The first stride earlier image processing is the calibration of the empirical model to transform colour spaces. As it can be seen in Eq. (two), the model's response was linear on the parameters αi , and then the fitting was easily performed. It is worth mentioning that the digital camera used in this work automatically changes its setting from scene to scene, so the colour chart was always included in the scene. The average full colour difference between the ColorChecker values and the predicted (Eq. 2) L*, a*, and b* values was ΔE = 2.35 ± 0.27, considering the 13 samples detailed in Table 1.

Table 1. L*a*b* colour values obtained from prototype processing and from spectrophotometer measurements.

Measurement of the characteristic colour

Figure two shows a digital image of an apple juice sample contained in the cell, equally it is viewed in the developed GUI used to process the digital images. Also the predicted colours obtained from its processing are depicted. Two conversion steps were involved, so every bit to display the colours in a visual sense: first, the epitome is transformed from RGB space to Fifty*a*b* using Eq. (2) and then from 50*a*b* back to RGB values using a straight conversion model.[3,xix] Every bit it can exist seen, a good correlation is observed.

To define the characteristic colour, the dependence of the predicted color values on the liquid depth was analysed. In this sense, Fig. 3 shows the Fifty*, a*, and b* colour values versus the liquid depth. From these results, it is clear that as the depth increases, each color value approaches a constant ane. These color values were used to fit Eq. (1) of this work. For this item sample, the average total colour difference of the fitting process was ΔE = 2.09 ± 0.69, using the 20 patches showed in Fig. 2. Comparable colour behaviours and fitting errors were obtained for the other samples. Similar color versus depth behaviour was reported by[ix] for olive oil samples using a spectrophotometer and a spectroradiometer. For wine samples[6] informed a similar L* behaviour and found circuitous Hue angle and Chroma variations. Tabular array ane shows the predicted characteristic colour of all the tested samples, using 20 equally spaced patches of x × x pixels each one. These results agreed well with preliminary experiments using a different jail cell,[32] made of cellular polycarbonate.

With comparison purposes, the colour values measured with the spectrophotometer are detailed in the same table. In general, the L* values from the spectrophotometer were considerably higher than those obtained from image processing, with an average absolute difference of 31.10. On the contrary, for most of the samples, a* and b* values from image processing were college (in accented values) than those measured by the spectrophotometer, existence the boilerplate absolute differences equal to 19.14 and 28.xxx, respectively. The correlation coefficients betwixt both methodologies were low: 0.59, 0.51, and 0.31 for L*, a*, and b*, respectively. Similarly, correlation coefficients for Hue angle and Chroma were 0.61 and −0.52, respectively. Because the whole data set except the red wine sample, the Hue angle had a expert correlation coefficient, equal to 0.98. In summary, the boilerplate total colour deviation ΔE between the measurements performed with both devices was l.85 ± 18.89.

To consummate this analysis, Tabular array 2 compares the colour appearance obtained from both procedures for 3 selected samples (grape juice, apple tree juice, and blue energy drink); also a picture of the samples in a test tube, using a white background and ten cm liquid depth, is shown. The color obtained from the image analysis methodology better resembles the samples. This result is due to the brusque depth of the spectrophotometer jail cell (1 cm); if deeper cells were used (equally modern spectrophotometer allow), the colour measured should be more similar to the characteristic colour.

Table two. Colour appearance obtained from epitome processing (colour at infinite depth) and spectrophotometer (1 cm jail cell) measurements for some selected samples. Likewise a picture of the sample into a examination tube is shown, using 10 cm liquid depth and a white groundwork.

Also, the ability of the proposed approach to analyse colour versus sample concentration was tested. In this sense, the predicted colour values at infinite depth of 2 randomly selected samples are shown: multi-fruit juice (Fig. 4) and blueish energy drink (Fig. 5), both diluted with distilled h2o to have dissimilar concentrations. To easily visualise these results, their predicted RGB values are included in the figures. Every bit information technology can be appreciated from these results, this methodology could be used to determine the sample concentration. In a like sense[5] related the colour of rooibos tea (measured with a spectrophotometer) with the solids content, using different prison cell path lengths. To avoid Hue angle inversion, the author recommended a 5 mm cell or diluted extracts. In general, the relationship between the concentration of a solute and the liquid color is better assessed using spectrophotometers (typically using a specified wavelength), whereas colour equally a quality attribute is better assessed from image analysis, since this method represents in a meliorate way the colour of the samples.

In add-on, the effect of the background colour was evaluated using the bluish energy drink sample. As it can be seen in Fig. half dozen, the three colour parameters tend to similar values every bit the depth increases, contained of the background colour. Eq. (1) was fitted for each individual pixel of the 20 patches, using 10 × 10 pixels in each patch, and thus 100 fitting procedures were performed for each curve. To complete this assay in a statistical style, Tabular array 3 details the average values and the standard deviations of the characteristic color predicted with the dissimilar backgrounds. At beginning, very similar values were obtained. However, the hypothesis test on the mean equality concluded that the influence of the background was significant because of the very low standard deviations of the measurements.

Tabular array 3. Characteristic colour predicted using different background, for absurd blue free energy drink sample.

Again, the 50*a*b* color values of the blueish free energy drink, measured with unlike backgrounds, were transformed to RGB values. These results are shown in Fig. vii. From this figure, it is evident that colour differences are notorious at small depths, and then the colour seems to remain constant. In this sense, Fig. eight shows the full colour difference ΔE (calculated with Eq. (3) assuming the initial colour value for each background as the reference) versus the jail cell depth z. This relationship was accurately fitted to an exponential equation: (half-dozen)

Effigy 7. Predicted colour variation with depth for the blue energy drinkable sample, using different background colours.

From this equation, a feature depth z, required to attain a desired total colour difference, is defined: (vii)

For instance, from Eq. (vii) the prison cell depth required to obtain 50% of variation on the total colour deviation ( ) for red and white wine was 4.5 and 37.ii mm, respectively. Hernández et al.[eight] estimated an average thickness of 3.6 mm of vino when measuring colour on the rim of a sample holder. It is of import to mention that for less translucent samples, the cell does not need be full filled, or small cells can exist used. On the contrary, for more translucent samples, a higher depth could exist necessary. Finally, in addition to predict the characteristic colour, defined equally the value predicted at space depth, the proposed methodology could be used to obtain the colour at an arbitrary depth, using a desired background.

Conclusion

In this work, a novel methodology to measure and characterise the color of translucent liquid foods was proposed. Typically, this property is measured using spectrophotometers. Nevertheless, with this methodology, the obtained colour does not resemble the colour equally it is perceived past the consumers. The colour of the liquid samples was measured using digital images, and the characteristic colour was defined as the colour at infinite depth. A cell with a tilted floor was designed and built advertising hoc, so that different depths could be evaluated simultaneously in a single epitome. An empirical mathematical conversion betwixt RGB and CIELAB color spaces was employed, as well as a standard color chart for scale purposes. The experimental results of color versus depth were fitted to an exponential equation, from which the characteristic Fifty*∞, a*∞ , and b*∞ color values were obtained. The proposed methodology allowed obtaining a successful colour prediction. Also, the total color difference ΔE versus the cell depth was accurately fitted to an exponential equation, thus defining a characteristic depth, an of import parameter in the design of measurement cells for specific liquid foods. In general, colour predictions obtained from the image processing methodology improve resemble the food samples in a visual sense, and for near samples, a high correlation between prototype assay and spectrophotometer Hue values was obtained.

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Source: https://www.tandfonline.com/doi/full/10.1080/10942912.2017.1299758