APPENDIXSix rats were fed for 6 days a diet containing 16% of casein as the sole protein. For the last 3 days they were kept in metabolism cages, and the urine and faeces for each day were collected.They show that data based upon the nitrogen intake and excretion for one day indicate the state of nitrogen balance as well as those based upon figures for 3 days.The results of a study of the nitrogen balance are shown in the table. Ranges, avevages, and standard deviations Values for six rats for three separate days Daily N intake, mg. 203-244, 2373=16*7 232-244, 242 f 4 ' 9 223-244, 241 +8.G Daily N excretion, mg. 121-187, 17Oh 25'4 127-190. I70 5 23.6 I 30-202, 173525.4 N balance yo of ingested (positive), mg. N retained 6 7 5 1 1 . 1 29 f 6.6 54-105. 22-41. 7 ' i 19.3 2 9 5 7 . 2 42-93, 17-42. 57-82, 23-40, 68f18.9 2 9 f 8 . 9 Values for six rats for three days combined 219-244, I 26-190, 54-93, 22-41, 240h10.2Custard apple pulp, when exposed to air, turns pink due to peroxidase activity and becomes bitter when heated above 55", whch renders preservation by heat treatment inapplicable. To preserve the pulp, it is necessary to add 1% of citric acid together with 0 .

No abstract

This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) & K-Nearest Neighbours (KNN) classifiers were the binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing food wastage.

No abstract