This study has been carried out aiming to have the materials for the use of selective control of specific functional component contents of vegetables and fruits by using LED light source in future through examination and analysis on the influence of LED light source (385 nm, 470 nm, 525 nm, and 630 nm) on the functional components and quality change of immature strawberry. Soluble solid content was 9.87% immediately after harvest and in 4 storage days, it gradually increased as 12.77% for the test group which is higher than control group of 10.97%. Acidity increased gradually during 3 storage days and then decreased a little at the 4 th day from the beginning of storage irrespective of LED treatment for every group under treatment. Vitamin C, anthocyanin and total phenol content increased gradually for 4 storage days due to LED treatment for control group and test group. Each nutrient was as 54.28 mg/100 g, 6.89 mg/100 g, and 129.5 mg/100 g immediately after harvest and in 4 storage days, they appeared to be 78.70 mg/100 g, 12.48 mg/100 g, and 172.75 mg/100 g for the test group, higher than that of control group which were 71.64 mg/100 g, 9.89 mg/100 g, and 151.00 mg/100 g. Consequently, LED irradiation seemed to have affected the nutrients and quality of strawberry.Additional key words: acidity, anthocyanin, soluble solid content, total phenol, vitamin C Hort.
Phlorotannins are reported to have diverse biological properties. However, no analytical methods for the standardization of phlorotannin preparations have been reported. Herein, we developed and validated an analytical method for the determination of dieckol in phlorotannin extracts (PRT) using high-performance liquid chromatography (HPLC). The optimum HPLC conditions consisted of a Supelco Discovery C18 column stationary phase, a mobile phase (A: 15 % HPLC grade methanol in deionized water, B: methanol), UV detection at 230 nm, and a flow rate of 0.7 mL/min. The optimized chromatographic conditions were validated and exhibited good specificity and linearity (R 2 > 0.9994-1.0000). The recoveries were in the range of 100.9-102.3 %. The method had good intermediate (%RSD 1.2) and intra-day (%RSD 0.4-1.7) assay precisions. This HPLC method had good accuracy and quality in the determination of dieckol in PRT.
Maintaining and monitoring the quality of eggs is a major concern during cold chain storage and transportation due to the variation of external environments, such as temperature or humidity. In this study, we proposed a deep learning-based Haugh unit (HU) prediction model which is a universal parameter to determine egg freshness using a non-destructively measured weight loss by transfer learning technique. The temperature and weight loss of eggs from a laboratory and real-time cold chain environment conditions are collected from ten different types of room temperature conditions. The data augmentation technique is applied to increase the number of the collected dataset. The convolutional neural network (CNN) and long short-term memory (LSTM) algorithm are stacked to make one deep learning model with hyperparameter optimization to increase HU value prediction performance. In addition, the general machine learning algorithms are applied to compare HU prediction results with the CNN-LSTM model. The source and target model for stacked CNN-LSTM used temperature and weight loss data, respectively. Predicting HU using only weight loss data, the target transfer learning CNN-LSTM showed RMSE value decreased from 6.62 to 2.02 compared to a random forest regressor, respectively. In addition, the MAE of HU prediction results for the target model decreased when the data augmentation technique was applied from 3.16 to 1.39. It is believed that monitoring egg freshness by predicting HU in a real-time cold chain environment can be implemented in real-life by using non-destructive weight loss parameters along with deep learning.
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