Kiwifruit Samples Research Articles

A Model for Selecting Kiwifruit (Actinidia eriantha) Germplasm Resources with Excellent Fruit Quality.

The evaluation of quality traits is an important procedure for kiwifruit breeding and comprehensive utilization. The present study aimed to establish a comprehensive system to assess Actinidia eriantha germplasms by analyzing 22 quality traits on kiwifruit samples collected from a wild population of 236 plants grown in the Jiangxi Province, China. Variability, correlation, principal components, and cluster analyses were carried out using the data collected from fruit quality evaluations. The coefficients of variation (CV) of fruit quality traits ranged from 11.66 to 66.16% (average coefficient = 35.09%), indicating a high level of variation among the 236 plants. In addition, different degrees of correlations were found between the traits, with similar traits demonstrating strong correlations. Principal component analysis (PCA) generated eight comprehensive and independent principal components, accounting for 77.93% of the original fruit quality information. Furthermore, an extensive evaluation from PCA ranked the plants based on cluster analysis and grouped them into seven categories. A stepwise regression analysis generated a prediction model, demonstrating a good fit (0.945) with the principal components of the comprehensive evaluation score. Overall, this study identifies nine quality traits, representing fruit appearance, sweetness, acidity, flavor, and nutritional attributes, as important traits for a comprehensive evaluation of A. eriantha fruits.

Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging.

Soluble solid content (SSC), firmness, and color (L*, a*, and b*) are important physicochemical indices for assessing the quality and maturity of kiwifruits. Therefore, this research aimed to realize the nondestructive detection and visualization map for the physicochemical indices of kiwifruits at different maturity stages by hyperspectral imaging coupled with the chemometrics. To further improve the detection accuracy and working efficiency of the models, competitive adaptive reweighted sampling (CARS) and successive projection algorithm were employed to choose feature wavelengths for predicting the physicochemical indices of kiwifruits. Multiple linear regression (MLR) was designed to develop simplified detection models based on feature wavelengths for determining the physicochemical indices of kiwifruits. The results showed that 32, 18, 26, 29, and 32 feature wavelengths were extracted from 256 full wavelengths to predict the SSC, firmness, L*, a*, and b*, respectively, with the CARS algorithm. Not only was the working efficiency of the CARS-MLR model improved, but the prediction accuracy of the CARS-MLR model for determining the physicochemical indices was also at its relative best. The residual predictive deviations of the CARS-MLR model for determining the SSC, firmness, L*, a*, and b* were 3.09, 2.90, 2.32, 2.74, and 2.91, respectively, which were all above 2.3. Compared with the model based on the full spectra, the CARS-MLR model could be used to predict the physicochemical indices of kiwifruits. Finally, the visualization map for the physicochemical indices of kiwifruits at different maturity stages was generated by calculating the spectral response of each pixel on the kiwifruit samples with the CARS-MLR model. This made the detection for the physicochemical indices of kiwifruits more intuitive. This study demonstrates that hyperspectral imaging coupled with the chemometrics is promising for the nondestructive detection and visualization map for the physicochemical indices of kiwifruits, and also provides a novel theoretical basis for the nondestructive detection of kiwifruit quality.

Application of carboxymethylcellulose in combination with essential oils nano-emulsions edible coating for the preservation of kiwifruit

The present research investigates the effectiveness of nano-emulsified coatings (C-1, C-2, and C-3) in preserving the kiwifruit at a temperature of 10 ± 2 °C with 90–95 % relative humidity (RH) for 30 days. The nano-emulsions were prepared from varied carboxymethyl cellulose (CMC) concentrations with different combinations of essential oils such as thyme, clove, and cardamom. Dynamic light scattering investigation with Zeta Sizer revealed that C-1, C-2, and C-3 nano-emulsions have nano sizes of 81.3 ± 2.3, 115.3 ± 4.2, and 63.2 ± 3.2 nm, respectively. The scanning electron microscopy images showed that the nanoemulsion of C-1 had homogenous spherical globules, C-2 had voids, and C-3 showed a non-porous structure with uniform dispersion. The X-ray diffraction analysis indicated that C-1, C-2, and C-3 nano-emulsion exhibited distinct crystallinity and peaks. The nano-emulsion C-1 had reduced crystallinity, while C-2 had lower intensity peaks, and C-3 had increased crystallinity. The results documented that compared to control kiwifruit samples, the samples coated with C-3 nano-emulsion have decreased weight loss, decay incidence, soluble solids, maturity index activity, ethylene production, total bacterial count, and increased titratable acid, and firmness attributes. The results of current research are promising and would be applicable in utilization in industrial applications.

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.

Rapid and Sensitive Detection of the Causal Agents of Postharvest Kiwifruit Rot, Botryosphaeria dothidea and Diaporthe eres, Using a Recombinase Polymerase Amplification Assay.

The occurrence of postharvest kiwifruit rot has caused great economic losses in major kiwifruit-producing countries. Several pathogens are involved in kiwifruit rot, notably Botryosphaeria dothidea, and Diaporthe species. In this study, a recombinase polymerase amplification (RPA) assay was developed for the rapid and sensitive detection of the pathogens responsible for posing significant threats to the kiwifruit industries. The RPA primer pairs tested in this study were highly specific for detection of B. dothidea and D. eres. The detection limits of our RPA assays were approximately two picograms of fungal genomic DNA. The optimal conditions for the RPA assays were determined to be at a temperature of 39°C maintained for a minimum duration of 5 min. We were able to detect the pathogens from kiwifruit samples inoculated with a very small number of conidia. The RPA assays enabled specific, sensitive, and rapid detection of B. dothidea and D. eres, the primary pathogens responsible for kiwifruit rots in South Korea.

Development of effective model for non-destructive detection of defective kiwifruit based on graded lines.

The accurate detection of external defects in kiwifruit is an important part of postharvest quality assessment. Previous studies have not considered the problems posed by the actual grading environment. In this study, we designed a novel approach based on improved Yolov5 to achieve real-time and efficient non-destructive detection of multiple defect categories in kiwifruit. First, a kiwifruit image acquisition device based on grading lines was developed to enhance the image acquisition. Subsequently, a kiwifruit dataset was constructed based on the external defect characteristics and a new data enhancement method was proposed to augment the kiwifruit samples. Thereafter, the SPD-Conv and DW-Conv modules were combined to improve Yolov5s, with EIOU as the loss calculation function. The results demonstrated that the improved model training loss value was 0.013 lower, the convergence was accelerated, the number of parameters was reduced, and the computational effort was increased. The detection accuracies of the samples in the test set, which included healthy, leaf-rubbing damaged, healed cuts or scarred, and sunburned samples, were 98.8%, 98.7%, 97.6%, and 95.9%, respectively, with an overall detection accuracy of 97.7%. The detection time was 8.0 ms, thereby meeting real-time sorting demands. The average detection accuracy and model size of SSD, Yolov5s, Yolov7, and Yolov5-Ours were compared. When the confidence threshold was 0.5, the detection accuracy of Yolov5-Ours was 10% and 6.4% higher than that of SSD and Yolov5s, respectively. In terms of the model size, Yolov5-Ours was approximately 6.5- and 4-fold smaller than SSD and Yolov7, respectively. Thus, Yolov5-Ours achieved the highest accuracy, adaptability, and robustness for the detection of all kiwifruit categories as well as a small volume and portability. These results can provide technical support for the non-destructive detection and grading of agricultural products in the future.

From Harvest to Market: Non-Destructive Bruise Detection in Kiwifruit Using Convolutional Neural Networks and Hyperspectral Imaging

Fruit is often bruised during picking, transportation, and packaging, which is an important post-harvest issue especially when dealing with fresh fruit. This paper is aimed at the early, automatic, and non-destructive ternary (three-class) detection and classification of bruises in kiwifruit based on local spatio-spectral near-infrared (NIR) hyperspectral (HSI) imaging. For this purpose, kiwifruit samples were hand-picked under two ripening stages, either one week (7 days) before optimal ripening (unripe) or at the optimal ripening time instant (ripe). A total of 408 kiwi fruit, i.e., 204 kiwifruits for the ripe stage and 204 kiwifruit for the unripe stage, were harvested. For each stage, three classes were considered (68 samples per class). First, 136 HSI images of all undamaged (healthy) fruit samples, under the two different ripening categories (either unripe or ripe) were acquired. Next, bruising was artificially induced on the 272 fruits under the impact of a metal ball to generate the corresponding bruised fruit HSI image samples. Then, the HSI images of all bruised fruit samples were captured either 8 (Bruised-1) or 16 h (Bruised-2) after the damage was produced, generating a grand total of 408 HSI kiwifruit imaging samples. Automatic 3D-convolutional neural network (3D-CNN) and 2D-CNN classifiers based on PreActResNet and GoogLeNet models were used to analyze the HSI input data. The results showed that the detection of bruising conditions in the case of the unripe fruit is a bit easier than that for its ripe counterpart. The correct classification rate (CCR) of 3D-CNN-PreActResNet and 3D-CNN-GoogLeNet for unripe fruit was 98% and 96%, respectively, over the test set. At the same time, the CCRs of 3D-CNN-PreActResNet and 3D-CNN-GoogLeNet for ripe fruit were both 86%, computed over the test set. On the other hand, the CCRs of 2D-CNN-PreActResNet and 2D-CNN-GoogLeNet for unripe fruit were 96 and 95%, while for ripe fruit, the CCRs were 91% and 98%, respectively, computed over the test set, implying that early detection of the bruising area on HSI imaging was consistently more accurate in the unripe fruit case as compared to its ripe counterpart, with an exception made for the 2D-CNN GoogLeNet classifier which showed opposite behavior.

基于可见/近红外高光谱成像无损预测猕猴桃可溶性固形物含量

Soluble solid content (SSC) is a major quality index of kiwifruits. Visible near-infrared (Vis/NIR) hyperspectral imaging with the genetic algorithm (GA) was adopted in this study to realize the non-destructive prediction of kiwifruit SSC. A laboratory Vis/NIR hyperspectral imaging system was established to collect the hyperspectral imaging of 120 kiwifruit samples at a range of 400–1100 nm. The average reflectance spectral data of the region of interest of the kiwifruit hyperspectral imaging were obtained after different preprocessing method, namely, Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), and their combination method. The prediction models of partial least squares regression, multiple linear regression, and least squares support vector machine (LS-SVM) were built for determining kiwifruit SSC by using the average reflectance spectral data and effective feature wavelength variables selected by GA, respectively. The results show that SG+MSC is the best preprocessing method. The precisions of the prediction models built using the effective feature wavelength variables selected by GA are higher than that established using full average reflectance spectral data. The GA-LS-SVM prediction model has a best performance with correlation coefficient for prediction (R=0.932) and standard error of prediction (SEP=0.536° Bx) for predicting kiwifruit SSC. The prediction accuracy has been improved by 5.6% compared with that of the prediction models established by using the full-band reflectance spectral data. This study provides an effective method for non-destructive detection of kiwifruit SSC.

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.

Actinidia arguta (Baby Kiwi) Waste: Preliminary Considerations on Seed Recovery

Fruit seed oils are of new interest due to their significant properties and can be a good opportunity to recover fruit waste. Actinidia arguta (baby kiwi) fruits are a novelty in the market and berries can be consumed with the peels. Due to their limited shelf life, fruits are very perishable and the waste management techniques used post-harvest are an important issue. Berry waste can be reused, for biological flows focused on food losses and waste reduction. Therefore, baby kiwi fruit samples were collected from the Ortofruititalia company orchards in Cuneo, Italy, and then processed and analysed for seed oil constituents using standard analytical methods. The results of this study indicate that unsaturated fatty acids were the most dominant fatty acids (92.6 g/100 g) in comparison with saturated (7.4 g/100 g). In addition, α-linoleic acid (82.7 g/100 g) was the most dominant unsaturated fatty acid. Additionally, γ-Tocopherol (0.023 g/100 g) was the most dominant tocopherol in this study. Extraction of seed oil from these berries could be proposed as an option for obtaining high-added-value oils for pharmaceutical cosmetics, among other uses.

Numerical and experimental study on the heat and mass transfer of kiwifruit during vacuum freeze-drying process

In this work, the heat and mass transfer characteristics of kiwifruit samples during vacuum freeze-drying process have been investigated numerically and experimentally. The numerical simulation with moving grid method is adopted to simulate the radial and axial moving sublimation interfaces during vacuum freeze-drying. The simulated average moisture content and temperature of kiwifruit during drying are validated with the measured data, with the mean average error (MAE) of 14.7%. The findings demonstrate that the sublimation interface shifts toward the center during the drying process and the sublimation rate and pressure gradient are significant in the early stage of drying. After 10 h of drying process, the sublimation rate dropped by 80% due to the increase of the thickness of the dried region and the decrease of the shelf plate heat transfer. In addition, it is noted that the increasing shelf plate temperature and chamber pressure would cause an increase of sublimation rate, thereby reducing the drying time. Although increasing the sample thickness can improve the sublimation rate, the drying time becomes longer due to the presence of more ice crystals in thicker kiwifruit samples.

Low-cost and handheld detector on soluble solids content and firmness of kiwifruit

Soluble solids content (SSC) and firmness are two important internal quality indices of kiwifruit. The traditional methods used to measure these properties are destructive, thus result in fruit wasting. To make the measurement nondestructive and simultaneous, a handheld detector on SSC and firmness of kiwifruit was developed based on multi-spectral technology. An STM32F429IGT6 was used as the microprocessor of the detector. Twelve light-emitting diodes (LEDs) at selected wavelengths (705, 735, 740, 780, 810, 835, 840, 860, 880, 890, 935, and 970nm) based on our previous works were used as light sources. A TSL2561 was used as optical sensor. The 12 LEDs surrounded the optical sensor with an illumination angle of 45°. A plane-convex lens was used to focus light emitted by LEDs. The LEDs, optical sensor, and plane-convex lens were fixed on a support. The diffuse reflectance multi-spectral were collected on 1532 kiwifruit samples, of which, 1149 samples were used to develop the partial least-squares regression models to predict SSC and firmness, and 383 samples were used to test the performance of the developed detector. The test experiment showed that the root-mean-square errors of the developed detector were 0.42 % and 1.11 N cm−2 for SSC and firmness, respectively. The cost of the detector’s hardware was no more than 100 USD, and its weight was 380 g. The measurement time on SSC and firmness of one sample was less than 2 s. The developed low-cost, precise, and handheld detector could be potentially used to improve kiwifruit production and management.

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging.

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the , and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the , , and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality.

Mesoporous perovskite-type La0.8Sr0.2Cu0.7Mn0.3O3/SiO2 nanocomposite-decorated-graphene-oxide nanosheets: Green synthesis and application in the sensitive determination of Morin in kiwi fruit samples

Perovskite nanocrystals (PNCs) are highly tolerant to defects, unlike metal chalcogenides, and do not need surface passivation to retain high quantum yields. Remarkably, the defect structures and trap states in perovskites are often found only in their conduction and/or valence bands and not in the mid-states of the bandgap. Such intrinsic features of them essentially boost their properties and largely favor their sensing applications. In the present research work, perovskite/graphene oxide nanosheets were used to modify the surface of a glassy carbon electrode to the determination of the Morin in the kiwifruit samples. For this purpose, perovskite nanosheets were synthesized using the sol-gel method and combined with graphene oxide, which was prepared using Hummer’s method, to prepare the modifier suspension. To ensure the effective synthesis of the perovskite nanosheets, the morphological study has been applied and the modified electrode was examined by various characterization techniques. The electrochemical behavior of Morin on the surface of the modified carbon glass electrode was investigated using the cyclic voltammetry (CV) technique. The analytical figure of merits including the linear range of 10–110 μM and a detection limit of 4.49 μM were obtained. Finally, the designed sensing assay was efficiently applied for the determination of Morin in kiwifruit. The results obtained from the prepared sensor indicate suitable analytical properties including low detection limits, wide linear range, and good performance for the determination of Morin. Moreover, to check the accuracy of the proposed assay, the recovery index of 97 % indicates high performance in real samples. Stability, repeatability, and high reproducibility of the modified electrode are other prominent analytical features of the designed sensor. Consequently, it was possible to verify that the sensing assay has the potential for being desirably utilized in the determination of Morin in various real samples.

Identification of Chilling Injury in Kiwifruit Using Hyperspectral Structured-Illumination Reflectance Imaging System (SIRI) with Support Vector Machine (SVM) Modelling

Accurate detection of chilling injury in kiwifruit is challenging because the symptoms are mainly manifested in the interior. This work reports a method for detecting the chilling injury of ‘Hongyang’ kiwifruit to provide nondestructive discrimination. Kiwifruit samples with varying levels of chilling injury were analyzed by a hyperspectral structured-illumination reflectance imaging (SIRI) system. After demodulation, direct current (DC) and alternating current (AC) images with spatial frequencies of 30, 60, and 120 m−1 were obtained and labeled as F30, F60, and F120. Predictive models were developed to optimize the preprocessing and modeling methods. Prediction models established the results of DC and AC with different spatial frequencies and were compared. The autoscale-support vector machine (SVM) models were optimal for AC at different spatial frequencies, and the multiplicative scatter correction (MSC)-SVM model was optimal for DC. The combined features of F30, F60, and F120, as well as the spectral features of DC, had better accuracy for classifying chilling injury. The optimal model of hyperspectral SIRI system for detecting chilling injury was the F30 based on combined features, with calibration accuracy of 98.1% and prediction accuracy of 94.2%. This study has shown that structured illumination had higher accuracy than uniform illumination in predicting chilling injury. Further, this approach allows the identification of kiwifruit with chilling injury using a hyperspectral structured-illumination reflectance imaging system.

Viromics Reveals the Viral Diversity in Cultivated and Wild Kiwifruit.

China, the center of origin of kiwifruit, has the largest kiwifruit cultivation and production area worldwide, and Shaanxi Province is the major kiwifruit-growing region in China. However, our knowledge of kiwifruit viruses is largely skewed toward their pathology in cultivated orchards, and little is known about viral diversity in wild kiwifruit. To determine the viral diversity in cultivated and wild kiwifruit, 32 cultivated kiwifruit samples from Shaanxi Province and 30 wild kiwifruit samples from the Qinling Mountains were collected and subjected to high-throughput sequencing in this study. Eleven known viruses were found among the 32 cultivated kiwifruit samples, and 8 known viruses and 2 new viruses were found among the 30 wild kiwifruit samples. One of the two new viruses, Actinidia yellowing virus 3 (AcYV3), a member of the genus Idaeovirus, may be associated with severe yellowing of kiwifruit leaves. In addition, more than 50 nearly full-length genome sequences of known viruses were obtained. The detection rates, recombination, and molecular variation of these viruses were further analyzed. The results obtained in this study provide valuable information for understanding the virome of cultivated and wild kiwifruit.

Non-contact fruit ripening monitoring using a radiofrequency passive resonator

A high sensitivity radiofrequency (RF) passive resonator is used to investigate the feasibility of fruit ripening assessment by non-contact sensing of fruit dielectric property changes. To do so, kiwifruit samples of similar size and ripeness are monitored during 14 days using the RF resonator, used as an inductive sensor. The recording of the sensor impedance enables monitoring of the fruit complex dielectric permittivity changes induced by the ripening process. These results are compared to mechanical tests carried out on the kiwifruit samples during ripening, which validate the ability of the proposed RF sensor to monitor the ripening process. These preliminary results open the way to developing a low-cost, non-contact and non-destructive assessment of the fruit ripening process. • A non-contact RF passive resonator is used to monitor kiwifruit ripening. • RF resonator enables the monitoring of kiwifruit complex dielectric properties. • Dielectric properties of the kiwifruit feature a monotonous increase with time. • Kiwifruit dielectric monitoring is consistent with visual observations and firmness.

AuNPs and graphdiyne nanocomposite as robust electrocatalyst for methyl parathion detection in real samples.

The present work describes a simple and rapid synthesis method of gold nanoparticles and graphdiyne (AuNPs@GDY) nanocomposites including porous structure. Moreover, the synthesized AuNPs@GDY material was decorated on the glassy carbon electrode (GCE) with a drop coating method to construct a non-enzymatic electrochemical pesticides sensor. The micro-morphology and elemental composition of the materials were characterized by transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS). The electrocatalysis and conductivity of the material were studied with cyclic voltammetry (CV) and impedance method, respectively. The properties of the sensor were investigated by CV and differential pulse voltammetry (DPV). The results showed that AuNPs@GDY exhibited excellent electrocatalytic ability for methyl parathion in a wide linear range (from 0.25ng/mL to 24.43μg/mL) and low limit of detection value (6.2pg/mL). Furthermore, the DPV method used in this paper was accurate and sensitive, and could be used for routine quality control of methyl parathion in kiwi fruit and tomato samples.

Estimating the stiffness of kiwifruit based on the fusion of instantaneous tactile sensor data and machine learning schemes

Measuring the ripeness of fruits is one of the critical factors in achieving real-time quality control and sorting of fruit by growers and postharvest managers. However, recent tactile sensing approaches for fruit ripeness detection have suffered setbacks due to: (1) the nonlinear relationship between the sensor output and the true stiffness of fruits; and (2) the angle of contact, referred to as the inclination angle, between the sensor and the outer surface of the fruit. In this paper, we propose a non-destructive tactile sensing approach for estimating the stiffness of fruits, using kiwifruit as a case study. Our sensor configuration is based on a three-probe piezoresistive cantilever beam, allowing us to obtain relatively stable sensor outputs that are independent of the inclination angle of the fruit surface. Our stiffness estimation approach is based on the combination of instantaneous sensor outputs with 63 regression-based machine learning models comprising of neural networks, Gaussian process, support vector machines, and decision trees. For experiments, we used several kiwifruit samples at diverse ripeness levels. The extracted sensor data was used to train the learning models over a 10-fold cross-validation technique, allowing us to find the nonlinear relationships between the instantaneous sensor outputs and the ground truth stiffness of the fruit. Our pairwise statistical comparison by the Wilcoxon test at 5% significance revealed the competitive performance frontiers of our approach for stiffness prediction; the Gaussian process kernel functions and the binary trees outperformed other models at a mean squared error (MSE) of 1.0 and 2×10−23, respectively. Most neural network models achieved competitive learning performance at MSE less than 10−5 and the utmost performance being a pyramidal class of feed-forward neural architectures. The results portray the potential of achieving accurate ripeness estimation of fruit using intelligent tactile sensors with fast machine learning schemes across the supply chain.

Determination of dazomet and its metabolite methyl isothiocyanate residues in plant-derived foods by gas chromatography-triple quadrupole mass spectrometry

基于气相色谱-三重四极杆质谱建立了同时快速测定植物源性食品中棉隆及其代谢物异硫氰酸甲酯残留量的方法。对样品前处理及色谱条件进行了优化,蔬菜、水果、谷物、坚果、茶叶和香辛料样品经乙酸乙酯提取,离心后上清液经乙二胺-N-丙基硅烷化硅胶、石墨化炭黑、十八烷基键合硅胶和无水硫酸镁分散固相萃取净化,经DB-17MS色谱柱(30 m×0.25 mm×0.25 μm)分离,采用EI源,在多反应监测(MRM)模式下检测,基质匹配外标法定量。在优化实验条件下,目标化合物在0.005~1 mg/L范围内的线性关系良好,相关系数均大于0. 99,方法定量限为0.01 mg/kg。空白样品在4个添加水平下的平均回收率为74.2%~117.2%,相对标准偏差(n=6)为2.8%~9.0%,方法的准确度和精密度符合农药残留测定要求。应用建立的方法对实验室日常样品大白菜、韭菜、豇豆、油麦菜、茄子、姜、芹菜、马铃薯、橙、猕猴桃、西红柿、辣椒、大米、茶叶、杏仁和孜然各6个进行检测,均未检出目标物。该方法具有操作简单、快速、灵敏、准确的特点,解决了已有方法中需采用两种前处理方法、两套设备进行检测的不足,能满足植物源食品中棉隆及其代谢物异硫氰酸甲酯的检测要求。