Food Chemistry and Analysis PDF

BWD22303 Food Chemistry and Analysis By Name / Title / Address  BWD22303 Food Chemistry and Analysis ASSOC. PROF. ChM Dr. NORHAYATI BINTI MUHAMMAD Department of Technology and Natural Resources Faculty of Applied Sciences and Technolo...

BWD22303 Food Chemistry and Analysis By Name / Title / Address  BWD22303 Food Chemistry and Analysis ASSOC. PROF. ChM Dr. NORHAYATI BINTI MUHAMMAD Department of Technology and Natural Resources Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Pagoh Educational HUB KM 1, Jalan Panchor 84600 Pagoh Johor, MALAYSIA 069742088 0127237295 [email protected]  3.1 Color specification systems including visual systems, instrumental measurement of color, tristimulus colorimeters and color spaces 3.2 Practical considerations in color measurement such as interaction of light with sample, instrument choice, color 3. COLOR difference equations and color tolerances and sample preparation and presentation  Class Activity 1 If you are given a chance to decorate a birthday cake, what is/are the main colour/colours will be your choice? Introduction FOOD COLORS First factor of attraction towards food is its color. It influence appetite and choice of food. Introduction What is color in food? According to FDA, color is “Any dye, pigment or substance which when added or applied to a food, drug or cosmetic, or to the human body, is capable (alone or through reactions with other substances) of imparting color” According Food Law and Regulation 1985 Introduction Why food colors? Introduction Availability of Food Colors: Introduction Natural-identical colors Synthetic colors Man made pigments which are Man made colors which are not also found in nature found in nature Eg: carotene, riboflavin Eg: Azo dyes Natural colors Inorganic colors Pigment made by living organisms Made up of mineral compounds Eg: saffron, carmine Eg: Titanium dioxide, gold, silver TYPES OF COLORS  Introduction Natural colors Synthetic colors Obtain from natural sources Obtained by chemical reaction Differentiate Processed by physical means Process by chemical reaction natural & Health problem No health harm synthetic colors Good consumer acceptability Less consumer acceptability Not uniform Color uniformity Less bright Highly colored May be less stable High stability – to light, O2, etc High microbial contamination Low microbial contamination Expensive Less costly Introduction Natural Food Colors Introduction Natural Food Colors Introduction Natural Food Colors Introduction Synthetic Food Colors Introduction Synthetic Food Colors Introduction Synthetic Food Colors Introduction Synthetic Food Colors Introduction Synthetic Food Colors Introduction Synthetic Food Colors Introduction Synthetic Food Colors – Risk Assessment Introduction Synthetic Food Colors – Risk Assessment Food Color Analysis 3.1 Color Specification Systems Visual Systems Munsell Color System The best known and most widely used visual color-ordering system. It was developed by A.H. Munsell in 1905. Food Color Analysis 3.1 Color Specification Systems - Visual Systems Munsell Color System It contain, Hue: Red, Yellow, Green, Blue and Purple, plus five (5) adjacent pairs, Green-Yellow, Yellow-Red, Red-Purple, Purple-Blue, and Blue-Green Value: The quality of color described by lightness and darkness, from white to gray to black. It designated from zero (absolute black) to ten (absolute white). Chroma: The quality that describes the extent a color differs from a gray of the same value. It is designated in increasing numbers starting with 0 (neutral gray) and extending to /16 or even higher. Eg: A change from pink to red Food Color Analysis 3.1 Color Specification Systems - Visual Systems Munsell Color System Hue: Red, Yellow, Green, Blue and Purple, plus five (5) adjacent pairs, Green-Yellow, Yellow-Red, Red-Purple, Purple-Blue, and Blue-Green Value: The quality of color described by lightness and darkness, from white to gray to black. It designated from zero (absolute black) to ten (absolute white). Chroma: The quality that describes the extent a color differs from a gray of the same value. It is designated in increasing numbers starting with 0 (neutral gray) and extending to /16 or even higher. Eg: A change from pink to red Food Color Analysis 3.1 Color Specification Systems - Visual Systems Munsell Color System Food Color Analysis 3.1 Color Specification Systems Instrumental Measurement of Color, Tristimulus Colorimeters and Color spaces The CIE (Commission Internationale de I’Eclairage or The International Commission on Illumination) is the main international organization concerned with color and color measurement. Standard illuminants for color measurement were first established in 1931 by CIE. Richard S. Hunter, Deane B. Judd, and Henry A. Gardner were among the pioneering scientists who in the 1940s were working to develop color-measuring instruments that would overcome the disadvantages of previous system developed by CIE. Food Color Analysis 3.1 Color Specification Systems Instrumental Measurement of Color, Tristimulus Colorimeters and Color spaces The Hunter color solid was first published in 1942 where, L indicated lightness coordinate, a indicated the red (+) or green (-) coordinate, and b indicated the yellow (+) or blue (-) coordinate. The Hunter L,a,b Color Space Food Color Analysis 3.1 Color Specification Systems Instrumental Measurement of Color, Tristimulus Colorimeters and Color spaces In 1976, the CIE officially adopted the modified system as CIELAB with the paramaters L*a*b*. The coordinate; L* indicated lightness (0 to 100) with 0 being black and 100 being white, a* indicated the red (+) and green (-) coordinate, and b* indicated the yellow (+) and blue (-) coordinate. The limits for a* and b* are approximately + or -80 Food Color Analysis 3.1 Color Specification Systems Instrumental Measurement of Color, Tristimulus Colorimeters and Color spaces https://youtu.be/nQGarIbqeKU https://youtu.be/0xsWjlL4qCo?si=2t61EtkDByIVDr8x Colorimeter Food Color Analysis 3.1 Color Specification Systems Instrumental Measurement of Color, Tristimulus Colorimeters and Color spaces Smartphone Application Colorimeter E.g.: Android iPhone Food Color Analysis 3.1 Color Specification Systems Instrumental Measurement of Color, Tristimulus Colorimeters and Color spaces Class Activity 2 Measure the value of L*, a* & b* of the fruit below; L* = ? L* = ? a* = ? a* = ? b* = ? b* = ? Banana A Banana B Food Color Analysis 3.1 Color Specification Systems Instrumental Measurement of Color, Tristimulus Colorimeters and Color spaces Class Activity 2 Measure the value of L*, a* & b* of the fruit below; L* = ? L* = ? a* = ? a* = ? b* = ? b* = ? Palm fruit A Palm fruit B Food Color Analysis 3.2 Practical Consideration in Color Management Interaction of light with sample When a sample is illuminated with light, there will be interaction of light with an object/sample; Specular light – Light for which the angle of reflection is equal to the angle of incidence. Smooth polished surface will appear glossy because of the high degree of specular reflection, While, rough surfaces will have a dull or matte appearance due to diffuse reflection Opaque sample will reflect light, Transparent samples will primarily transmit light, Translucent samples will both reflect and transmit light Ideal samples for color measurement will be flat, smooth, uniform, matte, and either opaque or transparent. Food Color Analysis 3.2 Practical Consideration in Color Management Interaction of light with sample Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance When colorimeter measurements are conducted under carefully controlled conditions, data with a high degree of precision can be obtained. In both industrial and research applications, the interest is primarily in how color dimensions deviate from a standard, or how they change from batch to batch, year to year, or during processing and storage. Color differences are calculated by substracting L*a*b* values for the sample from the standard. Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance Eg; ∆L* = L*sample – L*standard Positive ∆L* numbers will be lighter than the standard, and negative ∆L* numbers will be darker ∆a* = a*sample – a*standard Positive ∆a* numbers will be more “red” (or less “green”) than the standard, and negative ∆a* numbers will be more “green” (or less “red”) ∆b* = b*sample – b*standard. Positive ∆b* numbers will be more “yellow” (or less “blue”), and negative ∆b* numbers will be more “blue” (or less “yellow”) Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance Total Color Difference (∆E*) is calculated by the following equation: ∗ ∗𝟐 ∗𝟐 ∗ 𝟐 𝟏/𝟐 ∆𝑬 = (∆𝑳 + ∆𝒂 + ∆𝒃 ) Where, ∆ L* = L*sample – L*standard ∆ a* = a*sample – a*standard ∆ b* = b*sample – b*standard. Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance Eg: Calculate the total color difference (∆E*) of apple 2 with apple 1 (standard) with the given value of L*a*b*. Steps: 1. Calculate the color difference of L*, ∆L* 2. Calculate the color difference of a*, ∆a* 3. Calculate the color difference of b*, ∆b*, 4. Insert in the equation total color difference (∆E*) Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance Eg: Calculate the total color difference (dE*) of apple 2 with apple 1 (standard) with the given value of L*a*b*. Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance Eg: Calculate the total color difference (dE*) of apple 2 with apple 1 (standard) with the given value of L*a*b*. ∗ ∗2 ∗2 ∗ 2 1/2 ∆𝐸 = (∆𝐿 + ∆𝑎 + ∆𝑏 ) ∗ ∗2 ∗2 ∗ 2 1/2 ∆𝐸 = (4.03 + (−3.05) + 1.04 ) ∗ ∆𝑬 = 𝟓. 𝟏𝟔 Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance Class Activity 3 Class Activity 1: Calculate the total color difference (∆E*) of seri muka 2 and seri muka 1 (standard) based on the given value of L*a*b*. Steps: 1. Calculate the color difference of L*, ∆L* 2. Calculate the color difference of a*, ∆a* 3. Calculate the color difference of b*, ∆b*, Seri muka 1 L* = 70.92 Seri muka 2 L* = 85.70 4. Insert in the equation total a* = -23.90 a* = -30.45 color difference (∆E*) b* = 40.93 b* = 32.50 18.23 Food Color Analysis 3.2 Practical Consideration in Color Management Color Difference Equation and Color Tolerance Calculate the total color difference (∆E*) of the fruits Class Activity 4 and vegetables from Set 2 and Set 3 with Set 1 (standard) based on the given value of L*a*b* below. Food Color Analysis 3.2 Practical Consideration in Color Management Sample Preparation and Presentation For color measurement data to be at all useful, the numbers must be consistent and repeatable. Sampling of product must be done so that it is representative of the products, and prepared so that it represents the product’s color characteristics. The number of readings that need to be taken for acceptable repeatability is dependent on the nature of the sample. Food Color Analysis 3.2 Practical Consideration in Color Management Sample Preparation and Presentation Some practical tips and systematic protocol for consistent color measurement of different food categories (Gordon Leggett, 2008) 1. Transparent liquids should be measured with a sphere instrument, using a clear glass of plastic cell. A cell filled with distilled water can be used as a blank to negate the effects of cell and solvent. Cell path length is selected based on color intensity. Eg: A 20 mm cell is used for most colored liquids, with 10 mm cells for highly absorbing liquids. A very thin 2 mm cell may be appropriate for highly absorbent transparent liquids such as soy sauce. For nearly colorless liquids, a 50 mm cell may be necessary. For clear transparent liquids, a single measurement using a viewing area of 15 mm diameter or greater may be sufficient for good repeatability. Food Color Analysis 3.2 Practical Consideration in Color Management Sample Preparation and Presentation Some practical tips and systematic protocol for consistent color measurement of different food categories (Gordon Leggett, 2008) 2. Liquid samples with high solid This type of samples are translucent rather than transparent. They can be measured by transmission using a very thin 2 mm path length cell, or measured in reflectance. It is necessary to control the thickness of the sample so that it is effectively opaque. Food Color Analysis 3.2 Practical Consideration in Color Management Sample Preparation and Presentation Some practical tips and systematic protocol for consistent color measurement of different food categories (Gordon Leggett, 2008) 3. Solid food samples This type of samples are vary with respect to size, geometry, and uniformity. Some colorimeters, reflectance measurements can be taken directly on the sample. Ideally, the surface should be flat. Readings of an apple or orange may be distorted because of the “pillowing” effect, which is a result of the distorted reflectance values from the uneven surface. SUMMARY