Applications of Colorimetric Analysis for Food Industry

#### **Chapter 5**

## Application of Colorimetry in Food Industries

*Kakoli Dutta and Rosalin Nath*

#### **Abstract**

The acceptance of any food product be it raw, cooked or processed is first evaluated by its color, flavor and texture. Human visual perception cannot accurately measure a particular color intensity, it may vary due to various factors. Though visual color standards and assessment are used in food industries but with the recent advancement of technology the quality assessment procedure is shifting towards colorimetric analysis. Colorimetry is the technology used for color measurement. There are various techniques and color models used in colorimetry while in the food sector the CIE LAB and RGB color model is mainly used as it is the closest to the mechanism of a human eye. Color measurement is a complex subject and the consumer acceptability of a food product, post-harvest management and market statistics depends on it. This chapter provides a brief discussion about the type of colorants, importance of color in the food industry, different color scales used in colorimetry and the various applications.

**Keywords:** colorimetry, CIEL\*a\*b\*, RGB, color model, color space

#### **1. Introduction**

As long as a person has good eyesight colors can be perceived, but it is not possible to quantify colors with sound vision [1]. Visual color judgments are affected by various factors, such as lighting conditions, angle of observation, color blindness etc. The only consistent and subjective was of color quality control is to measure it by a standardized instrument [2]. The quantitative measurement of colors can be done by colorimetry. The science of color measurement is known as colorimetry. Colorimetry is the technology used for measuring a wide range of food product including fresh as well as processed food items. Colorimetry can be also used to measure qualitative and quantitative measurement of components like amino-acid, sugar, enzymes, polyphenols, antioxidants etc.

Food quality generally depends upon three major parameters- nutritional quality, sensory acceptability and safety measures. Sensory acceptability includes attributes like color, flavor, aroma, body texture, mouthfeel and cultural appropriateness [3]. When it comes to customer acceptability color is the most important parameter on basis of which the quality of the food material is judged [4]. Color is considered as the most important physical attribute of food; it is demonstrated as the indicator of

physical chemical and sensorial product quality [4, 5]. Food color can be assessed using either the human visual system or color measuring instruments. Comparing colored references in regulated lighting is a requirement for visual system. Color standards are frequently utilized as reference materials to carry out color analysis in this method. This method implies slower inspection and requires proper training and that is the reason it is recommendable to employ color measurement through instruments. The most used instruments to measure color are colorimeter such as Hunterlab colorimeter, Dr. Lange colorimeter, Minolta chroma meter.

#### **2. Classification of food colors**

The very first sensory attribute by which a food is judged is its color. Human senses are somehow trained to perceive food with certain colors to be fresh and if the color deviates from its expectation, the food is rejected. Food with esthetic color and flavor are likely to be accepted as nutritious and hence consumed. Natural pigments which are associated with fruits and vegetables are usually bright and vivid. During processing of foods these pigments often undergoes physical and chemical changes causing color degradation and thus reduces the esthetic appeal. This is the reason food colorants both natural and artificial are used to restore the original appearance [6]. To ensure color uniformity, to help preserve the visual indication and to provide an attractive appeal to food colors are used [7]. Both artificial and natural colors play an important role in enhancing the color intensity, storage and quality control. However, there are strict government guidelines on the usage limit of these colors to reduce the toxicological effect if any and to ensure the health and safety of the consumers.

#### **2.1 Natural**

Natural raw materials when used as a source for extracting coloring components are usually known as Natural colors. The examples include anthocyanin from red grapes, betanine from beet root, chlorophyll from spinach or any green leafy vegetables, cucurmin from turmeric, lycopene from tomatoes, capsorubin from paprika and so on [8]. Often organic solvents are used to extract these natural and water-insoluble colors like carotene from carrots. To enhance the pigment stability against oxidation certain antioxidants like tocopherols are used. Certain plant which are not used for human consumptions are used for color extraction such as lutein from marigold flower, bixin or annatto from seeds of *Bixa orellano*, chlorophyll from grass [9, 10].

Other non-food raw sources from where natural colors are extracted are carminic acid which is orange in color from cochineal, carotenoid from nonedible algae, blue color pigment from spirulina, genipin from *Gardenia jasminoides* and lycopene from fungal biomass [11]. Caramel is a natural color but it does not occur in nature, when edible carbohydrates or sugars are heated at high temperature in presence of acid, alkali or salts [12]. Sources and physical properties of few natural colors has been mentioned below.

#### *2.1.1 Anthocyanins*

Anthocyanins belongs to the group of compounds called flavonoid; it is a watersoluble pigment. Six aglycone anthocyanidins found in foods are namely pelargonidin, delphinidin, malvidin, cyanidin, petunidin and peonidin (**Figure 1**). Anthocyanins are *Application of Colorimetry in Food Industries DOI: http://dx.doi.org/10.5772/intechopen.112099*

#### **Figure 1.**

*Six most commonly found anthocyanins in foods [13].*

mainly found in fruits such as red/purple grapes, black currant, cherry, raspberry, cranberry, strawberry, blueberries, red/purple cabbages and so on [14, 15].

The factors effecting the color and stability of the color are the chemical structure, light, temperature, oxygen, enzymes, metallic ions and other phenolics. Use of anthocyanins as a colorant has its limitations due to its low stability and interaction with other constituents in the food matrix [13].

#### *2.1.2 Betalains*

Betalains are nitrogen containing, water-soluble pigments ranging from red to violet betacyanins and yellow to orange betaxanthins. It is synthesized from tyrosine which is an amino acid into betacyanin and betaxanthin. Betalamic acid structure is the common chromophore among all the betalain pigments [16]. These pigments absorb visible radiation from 476 to 600 nm with 537 nm being the optimum at pH 5.0. The color shift (bathochromic) from betacyanins to betaxanthins, that is from 50 to 70 nm occurs due to the aromatic rings of cyclo-3-(3,4-dihydroxyphenylalanine). Beetroot is considered to be the most common source for the extraction of the food colorant betanin. Besides there are other sources such as Malabar Spinach (*Basella rubra*), tuber of *Ullucus tuberosus*, leaves and grains Amaranthas, dragon fruit, cactus fruit etc. [17, 18]. The stability of Betalains depends upon pigment content, degree of glucosylation, water activity, pH, antioxidants, temperature and nitrogen in atmosphere [19].

#### *2.1.3 Carotenoids*

Carotenoids are pigments that is soluble in fat, the color of the pigment ranges from yellow-orange-red usually found in plants and some animal. Carotenoids can be widely categorized into carotenes and xanthophyl. Carotenoids mostly present in foods are α and β carotene, lycopene, lutein, β cryptoxanthin and xanthophyll [20]. Sources of β-cryptoxanthin, α and β-carotene are carrot, oranges, sweet potato, mango, apricot, green leafy vegetables, tangerine, pumpkins and palm fruits [21]. Sources of lycopene are tomatoes, pink fleshed guavas, red-papayas and watermelon.

Corn, pumpkin and butternut squash are the sources of xanthophyll [22]. The stability of carotenoid may be affected by processing (juicing, puree formation, high temp cooking), disruption of food matrix (peeling, shredding, slicing), storage conditions (presence of light, atmosphere), water activity, enzymes etc. [23].

#### **2.2 Artificial**

Artificial food colors also known as synthetic food colors are the chemical modification of specific precursor compounds. The sources can be both natural and chemical; for example, indigo carmine is a synthetic color but obtained by sulfonation of indigo which is a natural source, whereas azo dyes providing various shades of red, yellow blue or black are produced by complete chemical synthesis of aromatic amine [24]. Artificial colors have advantages over natural colors with respect to heat or light sensitivity, color stability and chemical interactions. Artificial color has high color intensity and also do not impart any flavor or taste of its own. The colors that can be used in food is authorized by the European Commission (EC) in Regulation. The colors list in **Table 1** are the permitted synthetic colors to be used in food.

#### *2.2.1 Tartrazine*

Tartrazine (E 102) is a yellow color pigment; it is a water-soluble mono-azo color. This color remains stable in acidic condition, it can withstand temperature as high as 200°C [26]. Tartrazine is made by diazotization of 4-amino benzenesulfonic acid along with 4,5-dihydro-5-oxo-1-(4-sulfophenyl)-1H-pyrazole-3-carboxylic acid or its esters (**Figure 2a**). The color was isolated as potassium, calcium and sodium salts [27]. The Acceptable Daily Intake (ADI) limit of tartrazine is 7.5 mg/kg body weight per day. It has been found to be significantly harmful for asthma patients and even children at a higher dose and that is the reason this color has been banned in few countries [28]. The absorption of tartrazine in human body is as low as 5% and rest of the fraction passes undigested and unabsorbed through urine via the kidney.


#### **Table 1.**

*Authorized azo-colors according to European Commission regulation [25].*

*Application of Colorimetry in Food Industries DOI: http://dx.doi.org/10.5772/intechopen.112099*

**Figure 2.** *Structural formula of azo-dyes—(a) Tartrazine, (b) sunset yellow and (c) Azorubine.*

#### *2.2.2 Sunset yellow*

Sunset yellow (E 110) is a petroleum based yellow to orange azo dye for coloring food with the chemical name disodium 2-hydroxy-1-(4-sulfonatophenylazo) naphthalene-6-sulfonate (**Figure 2b**). Ca and K salts of this dye are permitted to be used as food color with a max permissible limit of 500 mg/kg used in sauces, coatings, salami and sausages, 50 mg/kg in baked and confectionary item, 50 mg/l is beverages and sometimes in various cheese and cheese rinds [29]. According to Joint FAO/WHO Expert Committee on Food Additives (JECFA) and Scientific Committee on Food of the European Commission (SCF) the ADI of sunset yellow is 0–4 mg/kg [27].

#### *2.2.3 Azorubine/Carmosine*

Azorubine (E122) is a red color, its chemical name is disodium 4-hydroxy-3-(4 sulfonato-1-naphthylazo) naphthalene-1-sulfonate, **Figure 2c** shows the structure. The maximum permissible limit of this dye is 500 mg/kg in preparation of sauces, coatings, salmon substitutes, processed meat or 50 mg/l in case of non-alcoholic or other fruit-based beverages. This color can also be used in minimum amount in edible cheese rind and casings. According to WHO the ADI of azorubine is 0–4 mg/kg [30], reportedly it does not have any carcinogenic effects, it may cause allergy in rare instances to The WHO and FAO approved dosages [31].

#### **3. Importance of color measurement**

Over the past few decades, the overall living standard of people have improved which has led to an increase in the awareness of quality of food products. As the demand for food has increased, the rate of food adulteration and food fraud have also increased leading to health risk exposure [32]. This is the reason food quality assessment is an essential step to be followed to ensure public health and safety [33]. There are various parameters of the assessment such as appearance (size, shape, color), texture and smell. Among these attributes food color is considered to be the primary parameter to evaluate food quality [34, 35]. The visual monitoring is the most convenient inspection method. Human eye is capable of recognizing almost 1million different colors [36] but due to poor color memory human brain cannot recall the exact color intensity observed previously [37]. Visual color monitoring depends upon a lot of complex factors such as lighting, human perception, illumination angles and so on. Therefore, instrumental methods are considered to be optimum.

Color may be defined as the observation experienced by a person when the radiation from the visible spectrum (400–800 nm) comes in contact with the eye [38]. Whereas, colorant is the pigment which give the product its color. Three important elements required for the phenomena of color to manifest are—a colored item, a source of light in the visible spectrum and a person/observer. White light can be absorbed, reflected or scattered when it collides with the object to be viewed. The main factor which determines the color of the object is the selective absorption of a particular light wavelengths [39].

### **4. Color specification system**

The color estimation system can be of two types: Visual system and Instrumental methods.

#### **4.1 Visual system (Munsell color system)**

The best known visual color-ordering system was developed by A.H. Munsell in the year 1905 and thus known as Munsell system (**Figure 3**). In this system, there are 5 primary color such as red, yellow, green, blue and purple and five secondary pairs green yellow, red purple, blue green, purple blue and yellow red. These secondary colors are known as Hue. When the color quality is described by its lightness or darkness that is from white to gray and black it is known as Value. The Value is measured in the scale of 0 which is absolute black to 10 which is absolute white. Chroma describes the saturation of each color, for example addition of pure blue hue to a gray until it reaches original blue. This point is the saturation where there will be no more change in the shade [42].

**Figure 3.** *Munsell visual system (source: [40, 41]).*

#### **4.2 Instrumental methods (CIE color system)**

CIE color system is a well-known system to describe color, CIE (Commission Internationale de l'Eclairage) is an important and main international body concerned with color measurement. In most food industries, the color measurement instruments work on color-space system: L\*, a\*, b\* defined by CIE. This trichromatic system can match any color by combination of the three primary color that is red, green and blue (RGB) represented by X, Y and Z axis respectively. The CIE system uses the chromaticity diagram to identify a particular color (**Figure 4**). Uppercase X, Y and Z gives the tristimulus values while lowercase x, y and z represent red, green and blue respectively. The value of x can be calculated by the formula, x = X/(X + Y + Z) and the value of y and z can be obtained by substituting the value of x.

#### *4.2.1 Color models and color spaces*

Color models are the visualization of multidimensional models depicting the color spectrum.

**RGB** color model uses three primary colors namely red, green and blue which are varied at specific amount to produce a desired color. This model depicts the mapping of the red, green and blue colors as x, y and z axis. Combinations of the primary colors produces secondary color (**Figure 5**). The common applications of RGB model are Cathode ray tube, LCD, LED or a large monitor. In RGB color model there is a sensor which records the intensity of red, green and blue spectrum in each pixel. In case of food RGB coordinates are transformed into CIE L\*a\*b\* color space for improved calculation of the colors [43].

**Figure 4.** *CIE chromaticity diagram.*

**Figure 5.** *Secondary colors from RGB.*

**Figure 6.** *The cylindrical diagram of HSV [41, 44, 45].*

**CMYK and CMY** has the primary color Cyan, Magenta and Yellow and the "K" stands for the key. This is a device dependent model and the working is similar to the RGB color model. The cyan, magenta, yellow and the black key pigments transferred to white surface and subtract other color from the surface to finally get the product color.

**HSV** color model has three components. Hue, Saturation and Value integrated to form a geometry of a cylinder. Often Value is replaced by 'brightness' or 'lightness' called HSB or HSL respectively (**Figure 6**). Both are common cylindrical representation in the RGB color model. The angle around the cylinder passing through the centre is the measurement of hue. The distance from the central axis to the boundary measures the saturation. The difference of color along the height of the central axis is the Value, Lightness or Brightness [46].

### **5. Principle of colorimeter**

When a beam of incident light (I0) travels through a specific solution, some of the light is reflected (Ir), some of it is absorbed (Ia), and the remaining light is transmitted (It). This is the basis for the principle of a colorimeter.

Therefore, I0 = Ir + Ia + It.

The two fundamental rules of photometry that form the foundation of the colorimeter concept can be used to define this mathematical relationship between the amount of light absorbed and the material concentration.

According to Beer's Law, the concentration of the solute in the solution is directly proportional to the amount of light absorbed.

*Application of Colorimetry in Food Industries DOI: http://dx.doi.org/10.5772/intechopen.112099*

$$\mathbf{Log}\_{10} \text{ I}\_0/\text{I}\_{\text{t}} = \mathbf{a}\_{\text{s}} \mathbf{c}$$

Where, as = absorbency index; c = concentration of solution.

According to the Lambert's law, the length and thickness of the solution under examination directly affect how much light is absorbed.

$$\mathbf{A} = \log\_{10} \mathbf{I}\_0 / \mathbf{I}\_{\mathbf{t}} = \mathbf{a}\_{\mathbf{s}} \mathbf{b}\_{\mathbf{t}}$$

Where, A = Absorbance of test; as = Absorbance of standard; b = length/thickness of the solution.

The combined mathematical expression of Beer-Lambert Law is:

$$\mathbf{L} \mathbf{g}\_{10} \text{ I}\_0/\text{I}\_{\text{t}} = \mathbf{a}\_{\text{s}} \mathbf{b} \mathbf{c}$$

If b is kept constant by taking a cuvette or a standard cell, then:

$$\mathbf{Log\_{10} I\_0/I\_t = \mathbf{a\_sc}}$$

Where Absorption index is given by: as = A/cl.

In the combined mathematical expression of Beer-Lambert's Law,

A ! Absorbance or optical density of the solution.

c ! Concentration of the absorbing material (gm/lit).

l ! distance traveled by light in solution (cm).

In simple terms, the combined principle of Beer-Lambert's law states that the amount of light absorbed by a color solution is directly proportional to the concentration of the solution and the length of the light path through the solution, A ∝ cl.

Thus, A = ∈cl

Where, ∈ ! Absorption Coefficient

Colorimeter is used to measure concentration of a colored solution or compound. It works in the visible spectrum of light ranging from 400 to 800 nm. As discussed, the working principle is based on Beer-Lambert law. Any colored chemical compound taken as a sample in the cuvette absorbs some light and the amount of light absorbed is proportional to the concentration of that that particular compound in the sample. The filament lamp which is the source of light inside the colorimeter emits light and it passes through the cuvette made of glass with a fixed thickness. Portion of the light is absorbed and the rest of the transmitted light falls on the photoelectric detectors. These detectors measure the intensity of incident light and the amount of absorbed light is calculated (**Figure 7**).

#### **6. Colorimeters used in food industries**

Colorimeters usually measures the primary sources which emits light and secondary sources which reflects external light.

#### **6.1 Tristimulus colorimeter**

The tristimulus values are obtained optically and not mathematically as the apparatus has been created based on the cone cells present in the retina of a human eye. The three filters present in the instrument, functions like the three types of cone cells

**Figure 7.** *Basic components of colorimeter [47].*

**Figure 8.** *Tristimulus colorimeter [49].*

in human eye. A tristimulus colorimeter is made of three main components: (1) a light source, (2) color filters which can modify the incident or reflected light's energy distribution and (3) a photoelectric detector which can convert reflected ray into electrical output [48]. The measurement done on this colorimeter is usually comparative and that is the reason it is important to calibrate the standards using similar colors that is to be measured. **Figure 8** shows the schematic diagram of a 4 filter tristimulus colorimeter.

#### **6.2 Spectrophotometer**

Spectrophotometer measures the absorbance of light when an incident light passes through the colored sample solution. The basic principle of spectrophotometer almost similar to the colorimeter except that spectrophotometer can also measure the absorbance in visible (400–800 nm), UV region (200–400 nm) and Infrared region (700– 1000 nm) of the electromagnetic spectrum.

There are two types of spectrophotometers: single beam and double beam.

In case of single beam spectrophotometer, the absorption of the blank sample (reference sample) is measured and then in place of blank the sample to be analyzed is placed and the absorption of light is measured. The absorption value of the blank sample is subtracted from the absorption value of main sample to get the result.

In case of double beam spectrophotometer, the source of illumination source gets split into two beams after the light passes through the lens. One beam of light passes through the blank sample while the other passes through the original sample. Both the transmitted rays hit a photo detector and then it is recombined to reach the

**Figure 9.** *Double beam spectrophotometer.*

monochromator. The display section gives the reading by subtracting the blank sample from the original sample. A schematic diagram in **Figure 9** shows the double beam spectrophotometer. The cuvettes holding the sample are made of quartz and the light sources used are deuterium lamps for UV measurement, tungsten lamp for visible range. In recent advancement xenon arc lamps has come into use for both UV and visible spectrum.

#### **6.3 Spectrocolorimeter**

The basic principle of Spectrocolorimeter is quite similar to a spectrophotometer. Spectrocolorimeter are a type of colorimeter used to measure the color across the spectrum, it can analyze the reflectance and transmittance of the sample for wavelength in visible spectrum. It can provide the colorimetric data as CIE L\*a\*b\* values or X, Y, and Z tristimulus values [50].

#### **6.4 Densitometer**

Densitometer actually measure the color of any particular food or any other substance based on its density. The color perception of any substance to be measured may change depending upon how dense the substance it. The color intensity of a single thin layer of any object differs from a thick film or layer. This type of colorimeter measures the amount of red, blue and green object that gets reflected from the object. Densitometer uses the optical filters of red, green and blue and measures the transmission of cyan, magenta and yellow.

#### **6.5 Computer or machine vision**

Computer vision came into existence around 1970s when it was used as mimicking the human vision system in the field of photography, camera schematics and projections. After a few decades later around 2010 this technique was used in other possible hi-end tasks like machine learning, graphics, fingerprint recognition, face recognition, image processing and so on [51]. The computer vision system setup consists of a light source or an illumination device, a camera to process the image objects, a frame grabber for scanning the image, a computer or a microprocessor to store the images and the computation based on a specific application and lastly a

**Figure 10.** *Essential parts of a typical computer vision system [52].*

high-resolution monitor to see the image [52]. **Figure 10** shows the schematic diagram of a typical computer vision system setup.

This non-destructive method was found to be used in food industry for color grading, sorting, maturity, and freshness of fruits and vegetables [53]. Image processing algorithms has to be made based on parameters such as shape, color homogeneity, defects of any types of fruits or vegetables to be assessed [54].

#### **7. Application of colorimeter**

Colorimeter is the most common analytical technique used in biochemical estimation. It is used for quantitative estimation of protein, glucose, blood plasma, enzymes etc. Besides these biochemical estimations, colorimeter has a wide application of colorimetry in food industry. Over the last few years, the use of colorimeter has increased significantly to determine food color. Colorimeters are used for standardization of color, quality control inspection of various raw materials used such as meat, eggs, fishes, coffee, fruits and vegetables and color in every batch of final product specially in foods like jam, jelly, ketchup, beverages, baked goods, oil, sausages etc. Food products can be of different shape, size, granular or powder, solid and liquid of different viscosity. Thus, based on its optical characteristic food can be classified as opaque, translucent and transparent.

#### **7.1 Tomatoes and its products**

Tomato based products is thoroughly investigated on the basis of color than any other any other group of food color. Consumers usually associates freshness and maturity of raw tomatoes or the quality of its products by its redness, due to this reason the measurement of color is very important [55]. The grades of color to assess tomato juice, sauce, purees, ketchup or canned tomatoes was first done by MacGillivray by dating back during 1920s and 1930s on disc colorimeter [56, 57]. The measurement of the color was based on Munsell system. As described by Peng Wan, Roma and Pear varieties of tomatoes were taken having red, orange and green colors. About 150 images of the samples were taken. An image processing algorithm was developed using C++ and Matrox imaging library 9.0 (Matrox, Inc., Dorval, Canada).

*Application of Colorimetry in Food Industries DOI: http://dx.doi.org/10.5772/intechopen.112099*

**Figure 11.** *Setup capturing images of tomato [58].*

Based on the images taken the color value was extracted and finally to develop a model on basis of which the maturity of the tomatoes could be classified [58]. **Figure 11** shows the setup of the image capturing process.

#### **7.2 Fruits and its derivatives**

Several studies have been conducted on the physicochemical changes occurring during the ripening of fruits and its harvesting. Color variables of unripe or ripe, pre-climatic or senescent variety and mangoes with post-harvest rapid ripening process could be co-related using CIE L\*a\*b\* variables acquired by image analysis. This non-destructive method could adequately classify the ripening phases of mangoes [59].

Similarly, color content in the development of lemon skin such as chlorophyll, lutein and carotenoid content were measured with L\*, a\* and b\* [60].

Image analysis data interpretation was done for raspberries at different maturation stages. The color, total antioxidant and polyphenolic compounds were evaluated using spectrophotometric methods, the result could determine the nutritional properties of this fruits [61].

The study on the change of carotenoid content (color degradation) of orange juice after and before pasteurization process and on storage at different temperature was evaluated by the CIEL\*a\*b\*. This evaluation could detect the loss of carotenoid pigment and formation of brown color (browning) during pasteurization [62].

#### **7.3 Meat and meat products**

Meat color determination is a very crucial parameter of the quality assessment procedure as raw meat contains moisture, protein, vitamins and other nutrients which makes it susceptible to microbial spoilage. The color determines the freshness, tenderness and shelf life of the meat to be consumed. Freshly cut meat surfaces forms bright red color oxymyoglobin due to oxygenation from purple haem pigment myoglobin [63]. Oxymyoglobin further oxidizes to brownish color flesh metmyoglobin which is mostly rejected at the retailer and consumers. Measurement of the surface meat color was done using Munsell spinning discs in the year 1953 by Butler et al. [64], but with advancement in technology the process is done by using Minolta colorimeter, Video-meter lab (MSI) and machine vision.

#### **7.4 Eggs**

According to the study conducted by Milovanovic et al. [65] five species of eggs were taken: hen, duck, quail, goose and turkey. The samples were refrigerated for better separation of yolk and egg white [66]. At first the color of the egg shells was determined at seven random positions on each egg samples. After that the eggs were broken, the yolk and the albumin portion were separated and placed in a petri plate with white background. Color of all the three separated parts were measured colorimeter. The color reading was calculated as L\*a\*b, Whiteness Index (WI), Yellowing Index (YI) and total color difference (ΔE).

In case of hen's egg, the L\* value detected was L\* = 63.2 which was closer to the duck's yolk L\* = 62.0 and both the yolks were brighter than quail's egg yolk where the L\* value was L\* = 51.6. The intensity of red color was marked by the a\* value where the hen's egg showed the highest intensity of a\* = 4.0, and highest b\* value was b\* = 42.4. The egg yolk coloration depends on animal health, feed and capability to storage xanthophylls [67]. The feed of the bird determines the color of the egg yolk (yellow– red) which attributes to the amount of carotenoid and the antioxidant activity of the pigments [68]. The color of the yolk has a significant value from consumer point of view as the color is connected with its freshness and quality.

#### **7.5 Color change due to thermal degradation**

The changes in color due to heating can also be analyzed using colorimeter. The color change after heating cashew apple juice was quantified by a Spectrocolorimeter as L\*, a\* and b\* values by CIE. Apart from CIE the Total color difference (ΔE\*), Chrome (C\*) and Hue (h) were also calculated [69]. The result showed reduction in the redness (a\*) and yellowness (b\*) after the juice was heat treated at 90°C. The simultaneous darkening of the juice indicated reduction in L\* value, the total color change ΔE\* increased as the time and temp increased. According to a study conducted by Lee and Coates [70], ΔE\* ≈ 2 represent color change but if the change is ΔE\* > 3 then the product is unacceptable. Thus, consumer acceptability can be detected by colorimetric study.

#### **7.6 Browning**

Browning is a very important phenomenon in food processing such as baking, frying and drying effecting the flavor and color of the final product. Browning may result from enzymatic or non-enzymatic or Maillard browning occurring due to the oxidation of the phenolic compounds. Non-enzymatic browning is the chemical reaction between amino acid and reducing sugars by the impact of heat producing a golden brown to brown color with a desirable flavor. Formation of deep brown color while the food is charred leads to rejection of the product. Whereas, Enzymatic browning occurs by the action of enzyme polyphenol oxidase (PPO) which oxidizes the phenolic compounds in presence of air forming a pigment called melanin. Browning of fruits and vegetables may occur due to cutting, peeling or even due to any mechanical injury occurred. This browning reaction is undesirable and rejected by the consumers. It is beneficial and desirable only in case of coffee, cocoa, raisins, or tea when enzymatic browning contributes to the distinct color and flavor to the product.

The overall change is the brown color is denoted by Browning Index (BI) indicating the browning of food products due to presence of sugar. The CIE L\*a\*b\* color space is used as the color model as the physical indicator of browning [48].

#### **8. Conclusion**

The link between human visual perception and food quality indicator was always considered to be the food color since time immemorial. Thus, color is the most important attribute influencing customer acceptability, food choices and purchase pattern. Measurement and analysis of color is an important factor in food industry to determine the use of food colorant, determine the quality and prescribed limit of artificial color in food, manage postharvest losses, understand the quality of food, determine the end point of baking, setting the time and temperature of a baking unit, determine the incubation or fermentation time of any food and so on. Colorimeter plays the major role in determining all these attributes of food quality. Color order systems that are widely adopted by the food industry are the HunterLab system, the CIEL\*a\*b\* system, and the L\*C\*H\* system. Recent advances in the field of image processing to determine machine vision is the future reality. Artificial Intelligence (AI) and machine vision are applied on both agriculture and food industries for generation of data and as a quality control parameter.

#### **Acknowledgements**

The authors would like to express their sincere gratitude to the President and the Director of the institution for their constant motivation and encouragement. Their expertise and insight were invaluable in shaping our research and to complete this chapter.

#### **Conflict of interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

*Advances in Colorimetry*

### **Author details**

Kakoli Dutta\* and Rosalin Nath Institute of Engineering and Management, Kolkata, India

\*Address all correspondence to: kakoli.dutta@iem.edu.in

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Application of Colorimetry in Food Industries DOI: http://dx.doi.org/10.5772/intechopen.112099*

#### **References**

[1] Ohta N, Robertson A. Colorimetry: Fundamentals and Applications. England: John Wiley & Sons; 2006

[2] Giese J. Color measurement in foods. Food Technology Magazine, IFT. 2003; **57**(12):1-4

[3] Clydesdale FM, Ahmed EM. Colorimetry—methodology and applications. Critical Reviews in Food Science & Nutrition. 1978;**10**(3):243-301

[4] Iqbal A, Valous NA, Mendoza F, Sun DW, Allen. Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses. Meat Science. 2010;**84**(3): 455-465

[5] Fathi M, Mohebbi M, Razavi SMA. Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated Kiwifruit. Food and Bioprocess Technology. 2011;**4**:1357-1366

[6] Ghorpade VM, Deshpande SS, Salunkhe DK. Food colors. In: Food Additive Toxicology. Vol. 4. New York: Marcel Dekker Inc.; 1995

[7] Council, National Academy of Sciences. National Research, "Food Colors," Committee on Food Protection, Food and Nutrition Board, Division of Biology. 1971

[8] Carle R, Schweiggert R, editors. Handbook on Natural Pigments in Food and Beverages: Industrial Applications for Improving Food Color. UK: Woodhead Publishing; 2016

[9] Šivel M, Kráčmar S, Fišera M, Klejdus B, Kubáň V. Lutein content in marigold flower (*Tagetes erecta* L.) concentrates

used for production of food supplements. Czech Journal of Food Sciences. 2014

[10] Leal F, de Clavijo CM. Annatto: Botany and horticulture. Horticultural reviews. 2011;**39**:389-419

[11] Bauernfeind JC. Natural food colors. In: Carotenoids as Colorants and Vitamin A Precursors. New York: Academic Press; 1981

[12] Fierens T, Van Holderbeke M, Cornelis C, Jacobs G, Sioen I, De Maeyer M, et al. Caramel colour and process contaminants in foods and beverages: Part II–Occurrence data and exposure assessment of 2-acetyl-4-(1, 2, 3, 4 tetrahydroxybutyl) imidazole (THI) and 4-methylimidazole (4-MEI) in Belgium. Food Chemistry. 2018;**255**:372-379

[13] Rodriguez-Amaya DB. Natural food pigments and colorants. In: Bioactive Molecules in Food. Cham: Springer; 2019. pp. 867-901

[14] Wu X, Prior RL. Systematic identification and characterization of anthocyanins by HPLC-ESI-MS/MS in common foods in the United States: Fruits and berries. Journal of Agricultural and Food Chemistry. 2005; **53**(7):2589-2599

[15] Wu X, Beecher GR, Holden JM, Haytowitz DB, Gebhardt SE, Prior RL. Concentrations of anthocyanins in common foods in the United States and estimation of normal consumption. Journal of Agricultural and Food Chemistry. 2006;**54**(11):4069-4075

[16] Strack D, Vogt T, Schliemann W. Recent advances in betalain research. Phytochemistry. 2003;**62**(3):247-269

[17] Biswas M, Dey S, Sen R. Betalains from *Amaranthus tricolor* L. Journal of Pharmacognosy and Phytochemistry. 2013;**1**(5):87-95

[18] Vaillant F, Perez A, Davila I, Dornier M, Reynes M. Colorant and antioxidant properties of red-purple pitahaya (*Hylocereus* sp.). Fruits. 2005;**60**(1):3-12

[19] Herbach KM, Rohe M, Stintzing FC, Carle R. Structural and chromatic stability of purple pitaya (Hylocereus polyrhizus [Weber] Britton & Rose) betacyanins as affected by the juice matrix and selected additives. Food Research International. 2006;**39**(6): 667-677

[20] Holden JM, Eldridge AL, Beecher GR, Buzzard IM, Bhagwat S, Davis CS, et al. Carotenoid content of US foods: An update of the database. Journal of Food Composition and Analysis. 1999;**12**(3): 169-196

[21] Furtado JD, Siles X, Campos H. Carotenoid concentrations in vegetables and fruits common to the Costa Rican diet. International Journal of Food Sciences and Nutrition. 2004;**55**(2): 101-113

[22] Reif C, Arrigoni E, Schärer H, Nyström L, Hurrell RF. Carotenoid database of commonly eaten Swiss vegetables and their estimated contribution to carotenoid intake. Journal of Food Composition and Analysis. 2013;**29**(1):64-72

[23] Xianquan S, Shi J, Kakuda Y, Yueming J. Stability of lycopene during food processing and storage. Journal of Medicinal Food. 2005;**8**(4): 413-422

[24] Lück E, Kuhnert P, Gölitz H, Grosse L, Jäger A, Otterstätter G, et al.

Dictionary of Food Additives. Germany: Behr's Verlag; 1998

[25] Konig J. Food color additives of synthetic origin. In: Scotter MJ, editor. Color Additives for Food and Beverages. Woodhead Publishing; 2015. pp. 35-56

[26] Leulescu M, Rotaru A, Pălărie I, Moanţă A, Cioateră N, Popescu M, et al. Tartrazine: Physical, thermal and biophysical properties of the most widely employed synthetic yellow foodcolouring azo dye. Journal of Thermal Analysis and Calorimetry. 2018;**7**(8)

[27] EFSA, European Food Safety Authority. Guidelines on submission of a dossier for safety evaluation by the EFSA of active or intelligent substances present in active and intelligent materials and articles intended to come into contact with food. EFSA Journal. 2009:1208

[28] Wu L. Chapter 7—Analysis of food additives. In: Innovative Food Analysis. United Kingdom: Academic Press; 2021. pp. 157-180

[29] Scotter MJ. Methods for the determination of European Unionpermitted added natural colours in foods: A review. Food Additives and Contaminants. 2011;**28**(5):527-596

[30] FAO. WHO Expert Committee on Food Additives and World Health Organization and Others. Evaluation of Certain Food Additives and Contaminants: Twenty-Seventh Report of the Joint FAO. Geneva: World Health Organization; 1983;(1)

[31] EFSA. Scientific opinion of the panel on biological hazards on the maintenance of the list of QPS microorganisms intentionally added to food or feed. EFSA Journal. 2009;**7**(12): 1-93

*Application of Colorimetry in Food Industries DOI: http://dx.doi.org/10.5772/intechopen.112099*

[32] Banerjee D, Chowdhary S, Chakraborty S, Bhattacharyya R. Recent advances in detection of food adulteration. Food Safety in the 21st Century. 2017:145-160

[33] Jain A, Pradhan BK, Mahapatra P, Ray SS, Chakravarty S, Pal K. Development of a low-cost food color monitoring system. Color Research & Application. 2021;**46**(2):430-445

[34] Blasco J, Aleixos N, Moltó E. Machine vision system for automatic quality grading of fruit. Biosystems Engineering. 2003;**85**(4):415-423

[35] Cubero S, Aleixos N, Moltó E, Gómez-Sanchis J, Blasco J. Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology. 2011;**4**: 487-504

[36] Francis FJ, Markakis PC. Food colorants: anthocyanins. Critical Reviews in Food Science & Nutrition. 1989;**28**(4): 273-314

[37] Bartleson CJ. Memory colors of familiar objects. JOSA. 1960:73-77

[38] Berns RS. Billmeyer and Saltzman's Principles of Color Technology. England: John Wiley & Sons; 2019

[39] Wrolstad RE, Smith DE. Color analysis. Food analysis. 2017:545-555

[40] Quevedo R, Aguilera JM. Computer vision and stereoscopy for estimating firmness in the salmon (*Salmon salar*) fillets. Food and Bioprocess Technology. 2010;**3**:561-567

[41] n.d. CC BY-SA 3.0. Available from: https://commons.wikimedia.org/w/ index.php?curid=1955750

[42] Gupte VC. Expressing colours numerically. In: Colour Measurement. United Kingdom: Woodhead Publishing; 2010. pp. 70-87

[43] Markovic I, Ilic J, Markovic D, Simonovic V, Kosanic N. Color Measurement Of Food Products Using CIE L\*a\*b\* and RGB. Journal of Hygienic Engineering and Design. 2013;**4**(1):50-53

[44] SharkD. 2020. Available from: h ttp://en.wikipedia.org/wiki/File:HSV\_ color\_solid\_cylinder.png.

[45] Randall D, Charlotte NC. Instruments for the measurement of color. In: Color Technology in the Textile Industry. North Carolina: AATCC; 1997. pp. 9-17

[46] Manamohana K, Bijur G, Malarout N, Singla B, Pavithra S, Naik N. Applications of colour models in the food processing. Palarch's Journal of Archaeology of Egypt/Egyptology. 2020; **17**(9):3496-3512

[47] EFSA. Recent developments in the risk assessment of chemicals in food and their potential impact on the safety assessment of substances used in food contact materials. European Food Safety Authority Journal. 2014:1-28

[48] Pathare PB, Opara UL, Al-Said FA. Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Technology. 2013;**6**: 36-60

[49] Kumar V, Gill KD, Kumar V, Gill KD. Photometry: Colorimeter and spectrophotometer. In: Basic Concepts in Clinical Biochemistry: A Practical Guide. Switzerland: Springer; 2018. pp. 17-20

[50] Gallardo JC, Medina J. Interinstrumental agreement between spectrocolorimeters. In: 5th

Iberoamerican Meeting on Optics and 8th Latin American Meeting on Optics, Lasers, and Their Applications; Venezuela. SPIE; 2004;**5622**

[51] Kakani V, Nguyen VH, Kumar BP, Kim H, Pasupuleti VR. A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research. 2020;**2**: 100033

[52] Sun D-W, editor. Computer Vision Technology for Food Quality Evaluation. UK: Academic Press; 2016

[53] Bhatt AK, Pant D. Automatic Apple Grading Model Development based on Back Propagation Neural Network and Machine Vision, and Its Performance Evaluation. AI & SOCIETY: Springer Nature; 2015. pp. 45-56

[54] Laykin S, Alchanatis V, Fallik E, Edan Y. Image-processing algorithms for tomato classification. ASABE. 2002;**45** (3):851-858

[55] Francis FJ, Clydesdale FM. Food Colorimetry: Theory and Applications. West Port, USA: AVI Publishing Co. Inc.; 1975

[56] MacGillivray JH. Tomato color as related to quality in the tomato canning industry. Agricultural Experiment Station: Purdue University; 1931; **350**(33)

[57] MacGillivray JH. Studies of tomato quality—IV: Variability in quality and food value of tomatoes. Proceedings of the Indiana Academy of Science. 1928; **38**:159-164

[58] Wan P, Toudeshki A, Tan H, Ehsani R. A methodology for fresh tomato maturity detection using computer vision. Computers and Electronics in Agriculture. 2018;**146**:43-50

[59] Vélez-Rivera N, Blasco J, Chanona-Pérez J, Calderón-Domínguez G, de Jesús P-FM, Arzate-Vázquez I, et al. Computer vision system applied to classification of "Manila" mangoes during ripening process. Food and Bioprocess Technology. 2014;**7**:1183-1194

[60] Conesa A, Manera FC, Brotons JM, Fernandez-Zapata JC, Simón I, Simón-Grao S, et al. Changes in the content of chlorophylls and carotenoids in the rind of Fino 49 lemons during maturation and their relationship with parameters from the CIELAB color space. Scientia Horticulturae. 2019;**243**:252-260

[61] Rodríguez-Pulido FJ, Gil-Vicente M, Gordillo B, Heredia FJ, González-Miret ML. Measurement of ripening of raspberries (*Rubus idaeus* L) by near infrared and colorimetric imaging techniques. Journal of Food Science and Technology. 2017;**54**(9):2797-2803

[62] Wibowo S, Vervoort L, Tomic J, Santiago JS, Lemmens L, Panozzo A, et al. Colour and carotenoid changes of pasteurised orange juice during storage. Food Chemistry. 2015;**171**:330-340

[63] MacDougall D. Colour in Food. Cambridge, England: Elsevier Science; 2002

[64] Butler OD, Bratzler LJ, Mallmann WL. The effect of bacteria on the color of prepackaged retail beef cuts. Food Technology. 1953;**7**(10):397-400

[65] Milovanovic B, Tomovic V, Djekic I, Solowiej BG, Lorenzo JM, Barba FJ, et al. Color assessment of the eggs using computer vision system and Minolta colorimeter. Journal of Food Measurement and Characterization. 2021;**15**:5097-5112

[66] Dvořák P, Doležalová J, Suchý P. Photocolorimetric determination of yolk *Application of Colorimetry in Food Industries DOI: http://dx.doi.org/10.5772/intechopen.112099*

colour in relation to selected quality parameters of eggs. Journal of the Science of Food and Agriculture. 2009; **89**(11):1886-1889

[67] Baker R, Günther C. The role of carotenoids in consumer choice and the likely benefits from their inclusion into products for human consumption. Trends in Food Science & Technology. 2004;**15**(10):484-488

[68] An SY, Guo Y, Ma SD, Yuan JM, Liu GZ. Effects of different oil sources and vitamin E in breeder diet on egg quality, hatchability and development of the neonatal offspring. Journal of Animal Sciences. 2010;**23**(2):234-239

[69] Zepka LQ, Borsarelli CD, da Silva MAAP, Mercadante AZ. Thermal degradation kinetics of carotenoids in a cashew apple juice model and its impact on the system color. Journal of Agricultural and Food Chemistry. 2009; **57**(17):7841-7845

[70] Lee HS, Coates GA. LWT, effect of thermal pasteurization on Valencia orange juice color and pigments. Food Science and Technology. 2003;**36**(1): 153-156

### Section 5
