Tracing internal quality and aroma of a red-fleshed kiwifruit during ripening by means of GC-MS and E-nose

Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems

The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques

A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R 2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage.

Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics

Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1^st derivative, and Savitzky-Golay 2^nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits' firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky-Golay 1^st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R²) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R² values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage.

The Pollen Donor Affects Seed Development, Taste, and Flavor Quality in 'Hayward' Kiwifruit

To investigate how different species or ploidy level of pollen donors affects the fruit quality of kiwifruit, flowers of 'Hayward' kiwifruit (a hexaploid Actinidia deliciosa cultivar, 6x) were hand-pollinated with pollen from ten different male donors. Kiwifruit plants pollinated with four distant species-M7 (2x, A. kolomikta), M8 (4x, A. arguta), M9 (4x, A. melanandra), and M10 (2x, A. eriantha)-had a low fruit-setting rate and therefore were not investigated further. Of the other six treatments, kiwifruit plants pollinated with M4 (4x, A. chinensis), M5 (6x, A. deliciosa) M6 (6x, A. deliciosa) had a larger fruit size and weight than those pollinated with M1 (2x, A. chinensis) and M2 (2x, A. chinensis). However, pollination with M1 (2x) and M2 (2x) resulted in seedless fruits, having few small and aborted seeds. Notably, these seedless fruits had higher fructose, glucose, and total sugar and lower citric acid content. This resulted in a higher sugar to acid ratio compared to fruits from plants pollinated with M3 (4x, A. chinensis), M4 (4x), M5 (6x), and M6 (6x). Most volatile compounds increased in the M1 (2x)- and M2 (2x)-pollinated fruit. A combination of principal component analysis (PCA), electronic tongue, and electronic nose suggested that the different pollen donors significantly affected the kiwifruit's overall taste and volatiles. Specifically, two diploid donors had the most positive contribution. This was in agreement with the findings from the sensory evaluation. In conclusion, the present study showed that the pollen donor affected the seed development, taste, and flavor quality of 'Hayward' kiwifruit. This provides useful information for improving the fruit quality and breeding of seedless kiwifruit.

The terpene synthase (TPS) gene family in kiwifruit shows high functional redundancy and a subset of TPS likely fulfil overlapping functions in fruit flavour, floral bouquet and defence

Volatile terpenes are important compounds that influence fruit flavour and aroma of kiwifruit. Terpenes in plants also impact on the floral bouquet and defence against pests and pathogens in leaves and fruit. To better understand the overlapping roles that terpenes may fulfil in plants, a systematic gene, chemical and biochemical analysis of terpenes and terpene synthases (TPS) was undertaken in Red5 kiwifruit (Actinidia spp.). Analysis of the Red5 genome shows it contains only 22 TPS gene models, of which fifteen encode full-length TPS. Thirteen TPS can account for the major terpene volatiles produced in different tissues of Red5 kiwifruit and in response to different stimuli. The small Red5 TPS family displays surprisingly high functional redundancy with five TPS producing linalool/nerolidol. Treatment of leaves and fruit with methyl jasmonate enhanced expression of a subset of defence-related TPS genes and stimulated the release of terpenes. Six TPS genes were induced upon herbivory of leaves by the economically important insect pest Ctenopseustis obliquana (brown-headed leaf roller) and emission, but not accumulation, of (E)- and (Z)-nerolidol was strongly linked to herbivory. Our results provide a framework to understand the overlapping biological and ecological roles of terpenes in Actinidia and other horticultural crops.

Evaluation of Dynamic Changes and Regularity of Volatile Flavor Compounds for Different Green Plum (Prunus mume Sieb. et Zucc) Varieties during the Ripening Process by HS-GC-IMS with PLS-DA

Headspace gas chromatography-ion mobility spectrometry and partial-least-squares discriminant analysis (PLS-DA) were adopted to analyze the rule of change in flavor substances for different varieties of green plums at different levels of maturity (S1-immature, S2-commercially mature, and S3-fully mature). The results showed that 68 kinds of volatile flavor substances were identified in all green plum samples. The types and contents of such volatile substances experienced a V-shaped trend with an increasing degree of green plum maturity. During the S1 and S2 stages, aldehydes, ketones, and a small amount of alcohols were the main volatile flavor substances in the green plum samples. During the S3 stage, esters and alcohols were the most important volatile flavor components in the green plum pulp samples, followed by terpenes and ketones. YS had the most types and highest contents of volatile flavor substances in three stages, followed by GC and DZ. By using the PLS-DA method, this study revealed the differences in flavor of the different varieties of green plums at different maturity stages, and it identified eight common characteristic volatile flavor substances, such as ethyl acetate, 3-methylbutan-1-ol, and 2-propanone, produced by the different green plum samples during the ripening process, as well as the characteristic flavor substances of green plums at each maturity stage (S1-S3).

RFID-based sensing in smart packaging for food applications: A review

The global pandemic COVID-19 has led to an increase in the number of people purchasing food online, which has brought to a higher demand on the food supply chain. Such as the need to collect more information related to food safety and quality in real-time. Strengthening management of food logistics information flow can reduce food loss and waste and bring better quality and safety of food to consumers. In this review, the importance and applicability of RFID (Radio Frequency Identification) technology to smart food packaging are described. This study emphasizes the recent advancement of the RFID tags in humidity, temperature, gas, pH, integrity, and traceability sensor applications in connection with food packaging. RFID sensors are more suitable for smart packaging both in terms of sensing ability and data transmission. A simpler, low-cost, more robust and less power-demanding sensors network is the development direction of smart packaging in the future. Chipless RFID sensors have the potential to achieve these functions. But it still faces many challenges to be overcome. For example, biocompatible, cost, reading range, multi-tag collision, multi-parameter sensors, recycling issues, security and privacy of RFID system should be solved.

Smartphone-Based Image Analysis for Rapid Evaluation of Kiwifruit Quality during Cold Storage

As a vitamin C–rich fruit, choosing the eating time for kiwifruit with the best quality during the shelf period is still a problem for consumers. This paper mainly focuses on the correlation between cold storage time, quality indexes, volatile flavor compounds of postharvest kiwifruit and RGB value readouts from photos taken by mobile phone. Results indicated that the R to B ratio values (Central R/B) and B to G ratio values (Central B/G) of the central site of kiwifruit were strongly associated with storage time and all quality indicators. The central R/B was negatively correlated with titratable acidity, vitamin C and 2,6-Nonadienal contents and firmness and positively correlated with storage time, weight loss, soluble solids content, total soluble sugars, total plate counts and 1,3-Cyclooctadiene. We provide a novel and smart strategy to predict the shelf life and quality parameters of kiwifruit by capturing and calculating RGB values using a smartphone.

Preparation, Shelf, and Eating Quality of Ready-to-Eat “Guichang” Kiwifruit: Regulation by Ethylene and 1-MCP

The acceptance of kiwifruit by consumers is significantly affected by its slow ripening and susceptibility to deterioration. Ready-to-eat “Guichang” kiwifruit and its preparation technology were studied by the regulation of ethylene and 1-MCP. Harvested kiwifruits were treated with 100–2000 μl L⁻¹ethylene for 36 h (20°C) and then treatment with 0–0.5 μl L⁻¹ 1-MCP. The results showed that the preservation effect of 0.5 μl L⁻¹ 1-MCP is inefficient when the soluble solid content of kiwifruit exceeded 15%. The ethylene-treated fruits reached an “edible window” after 24 h, but a higher concentration of ethylene would not further improve ripening efficiency, while the optimal ethylene concentration was 250 μl L⁻¹. Moreover, after 250 μl L⁻¹ ethylene treatment, 0.5 μl L⁻¹ 1-MCP would effectively prolong the “edible window” of fruits by approximately 19 days. The volatile component variety and ester content of 0.5 μl L⁻¹ 1-MCP-treated fruits were not different from those of the CK group. Principal component analysis and hierarchical cluster analysis indicated that the eating quality of fruits treated with 0.5 μl L⁻¹ 1-MCP was similar to that of fruits treated with ethylene. Consequently, ready-to-eat “Guichang” kiwifruit preparation includes ripening with 250 μl L⁻¹ (20°C, 36 h) ethylene without exceeding the 1-MCP threshold and then treated with 0.5 μl L⁻¹ 1-MCP (20°C, 24 h). This study highlights the first development of a facile and low-cost preparation technology for ready-to-eat “Guichang” kiwifruit, which could reduce the time for harvested kiwifruit to reach the “edible window” and prolong the “edible window” of edible kiwifruit.

Quality changes and shelf-life prediction model of postharvest apples using partial least squares and artificial neural network analysis

The quality of postharvest apples is greatly affected by storage temperatures. In this paper, the sensory qualities, such as flavor, texture, color, and taste change of apples during storage at 4 °C and 20 °C were investigated. After correlation analysis, the partial least squares (PLS) and artificial neural network (ANN) techniques were used to build a shelf-life prediction model. The results showed that lower temperature storage can better maintain the color, flesh hardness, and release of volatile compounds of apples. The acidity of apples stored at 20 °C decreased much faster than that at 4 °C. The PLS models were successful in predicting the apple shelf life. When modeling using PLS with a single type index, the order of accuracy of the prediction model was texture, color, and flavor. As a nonlinear algorithm, the ANN model was also an effective predictive tool of apple shelf life at both temperatures.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R²), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

HS-SPME-GC-MS and Electronic Nose Reveal Differences in the Volatile Profiles of Hedychium Flowers

Floral fragrance is one of the most important characteristics of ornamental plants and plays a pivotal role in plant lifespan such as pollinator attraction, pest repelling, and protection against abiotic and biotic stresses. However, the precise determination of floral fragrance is limited. In the present study, the floral volatile compounds of six Hedychium accessions exhibiting from faint to highly fragrant were comparatively analyzed via gas chromatography-mass spectrometry (GC-MS) and Electronic nose (E-nose). A total of 42 volatile compounds were identified through GC-MS analysis, including monoterpenoids (18 compounds), sesquiterpenoids (12), benzenoids/phenylpropanoids (8), fatty acid derivatives (2), and others (2). In Hedychium coronarium 'ZS', H. forrestii 'Gaoling', H. 'Jin', H. 'Caixia', and H. 'Zhaoxia', monoterpenoids were abundant, while sesquiterpenoids were found in large quantities in H. coccineum 'KMH'. Hierarchical clustering analysis (HCA) divided the 42 volatile compounds into four different groups (I, II, III, IV), and Spearman correlation analysis showed these compounds to have different degrees of correlation. The E-nose was able to group the different accessions in the principal component analysis (PCA) corresponding to scent intensity. Furthermore, the pattern-recognition findings confirmed that the E-nose data validated the GC-MS results. The partial least squares (PLS) analysis between floral volatile compounds and sensors suggested that specific sensors were highly sensitive to terpenoids. In short, the E-nose is proficient in discriminating Hedychium accessions of different volatile profiles in both quantitative and qualitative aspects, offering an accurate and rapid reference technique for future applications.

Analysis of the Aroma Chemical Composition of Commonly Planted Kiwifruit Cultivars in China

The aroma chemical composition of commonly planted kiwifruit cultivars in China was analyzed. The combination of 2-octanone with 3-octanone was the most suitable dual internal standard for quantitative analysis in GC-MS. A total of 172 aroma components in 23 kiwifruit cultivars were detected, and ethyl butanoate, (E)-2-hexen-1-ol, and (E)-2-hexenal could be considered the core aroma components in kiwifruit, but still need further confirmation using Sensomics. E-nose could effectively distinguish different cultivars of kiwifruit. Clustering based on GC-MS and E-nose results tends to be consistent and demonstrate a certain degree of similarity. Kiwifruit cultivars with different flesh colors cannot be effectively distinguished by their aroma chemical compositions. Different species of kiwifruit can be distinguished to some extent by their aroma chemical compositions, but the effect was not satisfactory. These results could prove valuable in the breeding, planting, and marketing of kiwifruits.

Innovations in Smart Packaging Concepts for Food: An Extensive Review

Innovation in food packaging is mainly represented by the development of active and intelligent packing technologies, which offer to deliver safer and high-quality food products. Active packaging refers to the incorporation of active component into the package with the aim of maintaining or extending the product quality and shelf-life. The intelligent systems are able to monitor the condition of packaged food in order to provide information about the quality of the product during transportation and storage. These packaging technologies can also work synergistically to yield a multipurpose food packaging system. This review is a critical and up-dated analysis of the results reported in the literature about this fascinating and growing field of research. Several aspects are considered and organized going from the definitions and the regulations, to the specific functions and the technological aspects regarding the manufacturing technologies, in order to have a complete overlook on the overall topic.

Correlation between fruit weight and nutritional metabolism during development in CPPU-treated Actinidia chinensis 'Hongyang'

Forchlorfenuron, N-(2-chloro-4-pyridyl)-N-phenylurea (CPPU), is often used to promote fruit growth and improve production. The role of CPPU in kiwifruit growth has been established. However, the correlation between fruit weight and nutritional metabolism during development after CPPU treatments remains largely undetermined. Here, we surveyed the variations in weight and nutrient components of the 'Hongyang' kiwifruit (Actinidia chinensis) when CPPU was sprayed on fruit 25 days after anthesis. The CPPU application did not significantly influence the dry matter, soluble solids, starch, vitamin C or protein concentrations. However, the fresh weight, length and maximum diameter were significantly increased compared with the control. Moreover, in fruit of the same developmental stage, the fructose, glucose and soluble sugar levels increased after the CPPU treatment, compared with the control. On the contrary, citric, quinic and titratable acid concentrations decreased. However, a correlation analysis between fresh weight and the nutritional contents revealed that CPPU did not affect the concentrations of the most abundant organic acids (quinic and citric) and sugars (glucose, fructose and sucrose), compared with control fruit of the same weight. Therefore, CPPU applications enhance 'Hongyang' kiwifruit weight/size. However, there were no significant differences in the nutritional qualities of treated and untreated fruit having the same weights.