Detection of toxic and non-toxic sweet cherries at different degrees of maturity using an electronic nose

Development an electronic nose to recognize pesticides in groundwater

Timely detection of Groundwater pollution is essential to protect human health, especially for pesticide pollution. To solve this issue, we proposed a novel solution to realize the prediction of pesticide in groundwater by using the electronic nose (e-nose). The main work of this paper was divided into three steps: 1) checking whether sample was polluted by pesticides, 2) further predicting the pesticide type, brand and pollution degree when the sample was polluted by pesticides, and 3) optimizing the sensor array. Random forest was used to complete the first step, which had the best accuracy and sensitivity of 100 %. Support vector machine was applied to complete the second step, and the accuracy reaching 98.08 %. As for the third step, recursive feature elimination was used to optimize the sensor array. After optimization, the number of sensors was reduced from 26 to 8. In addition, the e-nose developed in this paper was compared with a commercial e-nose. The results showed that the cost of the developed e-nose was much lower than that of the commercial e-nose despite its slightly weaker prediction performance. Thus, this e-nose can be employed to recognize the pesticides in groundwater, and even can be integrated into the while drilling technology to realize the in-situ detection of groundwater.

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region's samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain.

Evaluation the effects of temperature and packaging conditions on the quality of button mushroom during storage using e-nose system

In this study, the effects of different packaging conditions on the quality of button mushrooms and some its chemical properties (pH and TSS) using an e-nose system equipped with ten sensors have been investigated. The button mushrooms were packaged using two types of films in two atmospheric modes. They were stored at 25 and 4 °C for ten days. During the storage, they were tested every other day. The results showed a mild increase in pH levels in all treatments during the ten days. Changes in TSS in ordinary polyethylene film-packed samples and ambient atmosphere at room temperature showed the highest value. Moreover, investigating the sensor response during the storage period showed that the most significant changes in the response of all sensors occurred in samples packed with polyethylene film and ambient atmosphere at 25 °C. Also, the scoring diagram of principal component analysis (PCA) showed that the completely distinct groups were detectable at two temperatures, two packaging films, and two different packaging atmosphere. At the same time, there was an overlap between the groups in six storage times. The support vector machine (SVM-C) and artificial neural network (ANN)classified the samples with 81 and 66% accuracy in six different storage times. The values of R 2 for predicting TSS and pH using PLS (partial least squares regression), MLR (multiple linear regression) and PCR (principal component regression) ranged between 51 and 68 and 54-59%, respectively, however prediction of TSS had a higher accuracy.

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HS-GC-IMS and PCA to Characterize the Volatile Flavor Compounds in Three Sweet Cherry Cultivars and Their Wines in China

The aim of this research was to characterize differences and sources of volatile flavor compounds by using headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and principal component analysis (PCA). Three sweet cherry fruits from different cultivars (cv. Tie, Van, and Lap) and their wines that were produced by the same yeast were detected. The results showed that 27 flavor compounds were identified in cherry fruits, including 10 alcohols, 7 esters, 7 aldehydes, 2 ketones, and 1 organic acid. Twenty-three flavor compounds were identified in cherry wines, including nine esters, eight alcohols, three aldehydes, two organic acids, and one ketone. In cherry fruits, aldehydes, several alcohols, and one ketone were the most prevalent in cv. Tie, and the majority of esters and alcohols in cv. Van. After fermentation, ethanol, butanol, butanal, ethyl propionate, propionaldehyde, 3-hydroxy-2-butanone, and acetic acid increased, whereas 1-hexanol, 3-methyl-3-buten-1-ol, 1-penten-3-ol, ethyl acetate, methyl acetate, (E)-2-hexenal and hexanal decreased. Few differences were detected in the type and content of volatile compounds in cherry wines from cv. Tieton (WT) and cv. Van (WV). Almost all aldehydes are derived from cherry fruits, which cannot be produced during wine-making, and other volatile compounds are almost all produced by saccharomyces cerevisiae. The volatile compounds of cherry wines were determined by row materials and fermentation cultures. Flavor fingerprints were established by HS-GC-IMS and PCA, which provided a theoretical foundation for the evaluation and improvement of flavor quality in cherry wine-making.

Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview

Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.

Rapid and Non-Destructive Techniques for the Discrimination of Ripening Stages in Candonga Strawberries

Electronic nose (e-nose), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and image analysis (IA) were used to discriminate the ripening stage (half-red or red) of strawberries (cv Sabrosa, commercially named Candonga), harvested at three different times (H1, H2 and H3). Principal component analysis (PCA) performed on the e-nose, ATR-FTIR and IA data allowed us to clearly discriminate samples based on the ripening stage, as in the score space they clustered in distinct regions of the plot. Moreover, a correlation analysis between the e-nose sensor and 57 volatile organic compounds (VOCs), which were overall detected in all the investigated fruit samples by headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS), allowed us to distinguish half-red and red strawberries, as the e-nose sensors gave distinct responses to samples with different flavours. Three suitable broad bands were individuated by PCA in the ATR-FTIR spectra to discriminate half-red and red samples: the band centred at 3295 cm⁻¹ is generated by compounds that decline, whereas those at 1717 cm⁻¹ and at 1026 cm⁻¹ stem from compounds that accumulate during ripening. Among the chemical parameters (titratable acidity, total phenols, antioxidant activity and total soluble solid) assayed in this study, only titratable acidity was somehow correlated to ATR-FTIR and IA patterns. Thus, ATR-FTIR spectroscopy and IA might be exploited to rapidly assess titratable acidity, which is an objective indicator of the ripening stage.

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.