Amalia Berna

Electronic nose as a non-destructive tool to evaluate the optimal harvest date of apples

Abstract An electronic nose (E-nose) has been evaluated for use as a tool to predict the optimal harvest date of apples ( Malus domestica Borkh.). The volatiles of ‘Jonagold’ and ‘Braeburn’ apples were assessed during the preclimacteric stage for two consecutive harvest years by means of an E-nose. A principal component data analysis indicated the presence of both a year and cultivar effect. Partial least square (PLS) models were constructed based on data of both harvest years. The cultivar effect made it difficult to build accurate and robust models for the two cultivars together. As a consequence, calibration models were constructed based on data of 2 years, but for each cultivar separately. The prediction of maturity, according to the Streif Index, showed a cross-validation correlation of 0.89 and 0.92 for ‘Jonagold’ and ‘Braeburn’ fruit, respectively. The calibration models for the prediction of the maturity, defined as the number of days before commercial harvest had a validation correlation of 0.91 for ‘Jonagold’ and 0.84 for ‘Braeburn’ fruit. Individual quality characteristics (soluble solids, acidity, starch and firmness) were predicted reasonably well. The calibration model for soluble solids content resulted in a consistent validation correlation over the results over 2 years (0.76 and 0.77). The starch and firmness were predicted with a validation correlation between 0.72 and 0.80. The prediction of the total acidity was poor (validation correlation of 0.66 and 0.69). It was also demonstrated that the type of validation influences the model prediction performance. Care should be taken when interpreting and using the models to predict the optimal harvest date for other years and cultivars.

High throughput flavour profiling of fruit

Publisher Summary For the analysis of the flavor of fruit and vegetables that require minimal sample preparation, there is a need for high-throughput techniques that are easy to operate at the lowest possible cost. The shorter commercial life cycle of fruit and vegetables and the increasing importance of flavor have necessitated the development of new high-throughput techniques for flavor analysis. This chapter describes the biology of aroma and taste perception by humans as this knowledge has inspired the development of biomimetic sensors, such as electronic noses and tongues. It introduces high-throughput spectroscopic techniques for measuring taste components, with an emphasis on Near-Infrared (NIR) spectroscopy. It also discusses the principle and applications of electronic tongues and describes new developments in high-throughput aroma profiling based on mass spectrometry and electronic noses. Biomimetic sensors use sensor arrays that generate complex signals when exposed to a headspace or immersed in juices. These signals are then analyzed by means of chemometric techniques and related to either sensory attributes of the fruit or vegetable or to individual flavor components. Electronic noses and tongues and spectroscopic techniques, such as (near) infrared spectroscopy require less sample preparation than traditional techniques and are faster. Some of them, in particular NIR spectroscopy, for measuring soluble solids content, are non-destructive and have been mounted on grading lines.

Mass Spectrometry Based Electronic Nose for Apple Aroma Profiling

The potential of the electronic nose (E-nose) and the mass spectrometer based electronic nose (MSE-nose) to monitor changes in aroma profile during shelf life was studied. Apples were stored for eight months at three different storage conditions and the aroma profile changes were followed subsequently over a period of 15 days. Gas chromatographic headspace analysis were conducted as reference measurements. A canonical variate (CV) analysis showed a clear effect of storage and shelf life both for the MSE-Nose measurements and the GC measurements. However for the E-Nose measurements only a shelf life but no storage history effect was observed. It was also found that the aroma profile changes during shelf life depended on the storage history. Partial least squares models were built to predict the apple firmness and the number of days in shelf life. It was found that the models based on the E-nose data had a worse prediction performance compared to those based on the MSE-nose data. Fusion of both sensors did not result in improved models. Both for firmness (0.95) and days of shelf life (0.98) a high cross validation correlation was observed between measured and predicted values. The SEP of firmness and shelf life were 2.38 Hz2 g2/3 × 106 and 1.02 days, respectively.