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Important changes occur in egg during storage leading to loss of quality. Prediction of these changes is critical in order to monitor egg quality and freshness. The aim of this research was to evaluate application of visible (VIS) and near infrared (NIR) spectroscopy as a rapid and non-destructive technique for egg quality assessment. Three hundred and sixty intact white-shelled eggs freshly laid by the same flock of hens fed with a standard feed were obtained. They were put under controlled conditions of temperature and humidity (T=18°C and RH=55%) for 16 days of storage. Forty eggs were analyzed at day 0, 2, 4, 6, 8, 10, 12, 14, and 16. Transmission spectral data was obtained in the range from 350 to 2,500 nm. The non-destructive spectral data was compared to egg sample's Haugh unit (HU) and albumen pH in terms of quality and to the number of storage days in terms of freshness. A partial least squares predictive model was developed and used to link the destructive assessment methods and the number of storage days with the spectral data. The correlation coefficient between the measured and predicted values of HU, albumen pH, and number of storage days were up to 0.94, R 2 was up to 0.90 and the root mean square error values for the validation were 5.05, 0.06, and 1.65, respectively. These results showed that VIS/NIR transmission spectroscopy is a good tool for assessment of egg freshness and albumen pH and can be used as a nondestructive method for the prediction of HU, albumen pH, and number of storage days. In addition, the relevant information about these parameters was in the VIS and NIR ranging from 411 to 1,729 nm.

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Artificial neural network (ANN) and hyperspectral techniques were used to model quality changes in avocados during storage at different temperatures. Avocados were coated using a pectin‐based emulsion and stored at different temperatures (10, 15, 20C), along with uncoated control samples. At different time intervals during storage period, respiration rate, total color difference, texture and weight loss of samples were measured as conventional quality parameters. Hyperspectral imaging was used to evaluate spectral properties of avocados. Multilayer ANNs were used in two ways to develop models for predicting quality parameters during storage. In the first set, ANN models were developed based on principal components of hyperspectral data as well as storage temperature and time. The optimal configuration of neural network model was obtained by varying the different model parameters. Results indicated ANN models to be accurate and versatile and they predicted the quality changes in avocado fruits better than the conventional regression models; furthermore, the storage time–temperature‐based ANN models were better than the hyperspectra‐based ANN models. PRACTICAL APPLICATIONS The manuscript evaluates the use of artificial neural network (ANN) models to relate the postharvest quality of avocados to traditional process variables like storage time and temperature. To provide a new objective method, data were also gathered from nondestructive hyperspectral radiometric technique as opposed to simple linking of quality of avocados to storage variables. ANN models were trained using both sets of data and were compared. The study demonstrated the ANN models to be more accurate in predicting the quality changes in avocado fruits than the conventional regression models; and furthermore, the storage time–temperature‐based ANN models were better than the hyperspectra‐based ANN models. ANN techniques are currently being used in many food process‐modeling applications. The study demonstrates the importance of new modeling techniques in predicting the quality of stored produce as well as the validity of well‐recognized storage parameters like temperature and time to be more important in quality considerations.

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