# Image Processing and Analysis for Biotechnology and Bioprocess Engineering
## 2 An Overview of Published Articles  
The article by [PERSON] et al. (Contribution 1) analyzed the starch content of six potato cultivars through image analysis, presenting a case study where image analysis could be employed in food processing. For analysis, the authors sliced the potatoes to a thickness of 3 mm and stained them by soaking them in a potassium iodide (KI) solution. Higher starch content resulted in dark staining with iodine, while lower starch content exhibited a lighter color. Instead of using a digital camera, the authors utilized a scanner equipped with LED lights on top. The obtained images were then converted into brightness-corrected gray images. Although the average gray value of the potato cross-section could be used for starch content analysis, the authors uniquely positioned four cross-section lines at 45\({}^{\circ}\) intervals on the potato. The brightness distribution of pixels along these lines was then analyzed. In addition to the mean value, the median, range, skewness, and coefficient of variation (CV) of the gray pixels located on these measurement lines were computed. For that purpose, ImageJ software was employed for this image processing. The analysis revealed a good negative linear correlation between the mean gray value of cultivars and their actual starch content. Furthermore, while the actual starch content of the six cultivars was somewhat similar, making cultivar differentiation challenging, the image analysis statistical values mentioned above allowed for better discrimination. This image analysis method also proved useful in distinguishing potatoes stored at different temperatures (4 \({}^{\circ}\)C vs. \(-\)15 \({}^{\circ}\)C), demonstrating its potential as a practical on-site technology in potato-processing plants. The study by [PERSON] et al. (Contribution 2) utilized image analysis techniques to elucidate the correlation between genetic variants (SNPs: Single-Nucleotide Polymorphisms) and facial skin phenotypes. In this study, five facial skin phenotypes, such as wrinkles, pigmentation, moisture content, oil content, and sensitivity, were analyzed. Among these, wrinkles and pigmentation require analysis using special imaging equipment. First, the wrinkle phenotype was measured by calculating the average roughness (Ra) and maximum depth (Rmax) of the uneven skin surface. For this purpose, optical three-dimensional (3D) imaging was performed using the PRIMOS (Phaseshift Rapid In-vivo Measurement Of Skin) CR device (Canfield Scientific, Parsippany, NJ, USA). This equipment projects fringes made of parallel lines onto the skin surface and then photographs them with a digital camera pointed at an angle. When photographed at an angle, the fringe lines are observed to be curved depending on the depth of the surface. By interpreting this, a 3D-depth image can be obtained [26; 27]. The pigmentation phenotype was analyzed for skin brightness and melanin content. For melanin measurement, the Mexameter MX 18 (Courage and Khazaka Electronic GmbH, Koln, Germany) was used. This equipment uses spectrophotometer technology to measure light in three wavelength bands (green at 568 nm, red at 660 nm, and infrared at 870 nm). Melanin content was quantified by measuring reflection and absorption in the skin, and skin brightness was assessed using a portable spectrophotometer, the CM-700d (Konica Minolta Inc., Tokyo, Japan), capable of full spectrum scanning from 400 nm to 700 nm. The L* value in the CIE L*a*b* color space was measured to serve as an indicator of skin brightness. Finally, the measured skin phenotype values were divided into tertile groups and successfully used to discover their relationship with genetic variation. The article by [PERSON] and [PERSON] (Contribution 3) studied the fluorescence decay characteristics of photosynthetic phycobiliprotein complexes extracted from _Spirulina maxima_. Phycobiliproteins, including C-phycocyanin (C-PC), have a variety of applications, including the food industry, cosmetics, medical products, sensors, and photo harvesting.