Changying Li

Detection of Apple Deterioration Using an Electronic Nose and zNosetm

Damage in apples can cause fruit spoilage, reduce commodity economic value, and give rise to food quality and safety concerns. This research investigated use of electronic nose (Enose, Cyranose 320) and zNoseTM -based nondestructive protocols for rapid detection of deterioration in apples. Key compounds associated with apple aroma were identified using gas chromatography and mass spectrometry, and the differences were observed after 6 days exposure to artificially induced damage in the form of a cut. High-dimensional data were compressed by principal component analysis (PCA) and partial least squares (PLS). Linear discriminant analysis (LDA) and canonical variate analysis (CVA) models were developed based on the compressed data. Experiments showed that both the Enose and zNose were able to effectively detect the volatile differences between undamaged and damaged apples four or more days after the cut. Differences in number of cuts had some effect on volatile compound emissions. Apples subjected to two cuts and three cuts generated volatile profiles that were significantly different from uncut apples. Varying the orientation of cut apples did not give significant differences in the volatile profile. The PLS-LDA model produced the best correct classification rates: 96% using the zNose, and 85% using the Enose.

A Customized Metal Oxide Semiconductor-Based Gas Sensor Array for Onion Quality Evaluation: System Development and Characterization

A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped with a gas delivery system to pump volatiles from the onion samples into the chamber. The electronic circuit mainly comprised a microcontroller, non-volatile memory chip, and trickle-charge real time clock chip, serial communication chip, and parallel LCD panel. User preferences are communicated with the on-board microcontroller through a graphical user interface developed using LabVIEW. The developed gas sensor array was characterized and the discrimination potential was tested by exposing it to three different concentrations of acetone (ketone), acetonitrile (nitrile), ethyl acetate (ester), and ethanol (alcohol). The gas sensor array could differentiate the four chemicals of same concentrations and different concentrations within the chemical with significant difference. Experiment results also showed that the system was able to discriminate two concentrations (196 and 1964 ppm) of methlypropyl sulfide and two concentrations (145 and 1452 ppm) of 2-nonanone, two key volatile compounds emitted by rotten onions. As a proof of concept, the gas sensor array was able to achieve 89% correct classification of sour skin infected onions. The customized low-cost gas sensor array could be a useful tool to detect onion postharvest diseases in storage.

An engineering and economic evaluation of quick germ–quick fiber process for dry-grind ethanol facilities: Analysis

An engineering economic model, which is mass balanced and compositionally driven, was developed to compare the conventional corn dry-grind process and the pre-fractionation process called quick germ-quick fiber (QQ). In this model, documented in a companion article, the distillers dried grains with solubles (DDGS) price was linked with its protein and fiber content as well as with the long-term average relationship with the corn price. The detailed economic analysis showed that the QQ plant retrofitted from conventional dry-grind ethanol plant reduces the manufacturing cost of ethanol by 13.5 cent/gallon and has net present value of nearly

A novel instrument to delineate varietal and harvest effects on blueberry fruit texture during storage

4 million greater than the conventional dry-grind plant at an interest rate of 4% in 15years. Ethanol and feedstock price sensitivity analysis showed that the QQ plant gains more profits when ethanol price increases than conventional dry-grind ethanol plant. An optimistic analysis of the QQ process suggests that the greater value of the modified DDGS would provide greater resistance to fluctuations in corn price for QQ facilities. This model can be used to provide decision support for ethanol producers.

Nondestructive Detection and Quantification of Blueberry Bruising using Near-infrared (NIR) Hyperspectral Reflectance Imaging.

BACKGROUND Firmness is an important quality index for blueberries. It is the major factor that determines consumer acceptability, storability and resistance to injury and diseases during storage and fresh marketing. Blueberry cultivars vary in their firmness, with southern highbush cultivars usually softer than Rabbiteye blueberries. In this study, varietal and harvest effects on blueberry firmness were measured by the Firmtech II and laser air-puff instruments. This was the first time that the laser air-puff, a non-contact food firmness tester, had been used for firmness testing of small fruit, such as blueberry. RESULTS Two southern highbush cultivars (Sweet Crisp and Emerald) and two Rabbiteye cultivars (Vernon and Savory) were used for varietal effect measurement, while a Rabbiteye cultivar (Premier) that was both machine and hand harvested was used for harvest effect observation. Fifty berry samples per replicate and four replicates were tested by two instruments at harvest and after 7, 14, or 21 days of storage. The laser air-puff tester successfully delineated the difference in firmness due to cultivar characteristics and harvest methods, as well as the firmness loss over 21 days of postharvest cold storage (4 °C). The firmness index derived from the laser air-puff tester achieved a significant correlation with the firmness values measured by the Firmtech (R(2)=0.80). A new texture index, springiness, was developed from the laser air-puff, which largely reflects the varietal differences in elasticity of fruit. CONCLUSION This study demonstrated the efficacy of the laser air-puff instrument for blueberry firmness measurement. This non-contact instrument not only provides an alternative method of firmness measurement, but also offers a new index for fruit elasticity evaluation and better texture evaluation for blueberries.

Detection of Apple Defects Using an Electronic Nose and zNose

Currently, blueberry bruising is evaluated by either human visual/tactile inspection or firmness measurement instruments. These methods are destructive, time-consuming, and subjective. The goal of this paper was to develop a non-destructive approach for blueberry bruising detection and quantification. Experiments were conducted on 300 samples of southern highbush blueberry (Camellia, Rebel, and Star) and on 1500 samples of northern highbush blueberry (Bluecrop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evaluation. An algorithm was developed to automatically calculate a bruise ratio index (ratio of bruised to whole fruit area) for bruise quantification. The spectra of bruised and healthy tissues were statistically separated and the separation was independent of cultivars. Support vector machine (SVM) classification of the spectra from the regions of interest (ROIs) achieved over 94%, 92%, and 96% accuracy on the training set, independent testing set, and combined set, respectively. The statistical results showed that the bruise ratio index was equivalent to the measured firmness but better than the predicted firmness in regard to effectiveness of bruise quantification, and the bruise ratio index had a strong correlation with human assessment (R2 = 0.78 − 0.83). Therefore, the proposed approach and the bruise ratio index are effective to non-destructively detect and quantify blueberry bruising.

Detection and discrimination of cotton foreign matter using push-broom based hyperspectral imaging: system design and capability.

Apple defects and spoilage not only reduce commodity economic value, but cause food safety concerns as well. It is essential for fruit quality assurance and safety to rapidly detect fruit physical damage and spoilage. This article presents the application of an electronic nose (Cyranose 320) and zNose to the development of a nondestructive, rapid and cost effective system for the detection of defects of apples. The key compounds associated with apple aroma were identified and the “smellprints” of these key compounds were established by the electronic nose and zNose. Healthy and damaged apples were kept in 2L glass jars for 6 hours for preconcentration before measuring. Principal Component Analysis (PCA) models were developed based on the Enose and zNose data. Maholanobis distance was applied for discriminant analysis. Experiments showed that the Enose and zNose are both capable of detecting the volatile differences between healthy apples and damaged apples. After five days deterioration, the correct classification rate for the Enose was 83.3%, and for the zNose was 100%. After seven days, the correct classification rate was 100% for both instruments. For the next stage, a non-linear model and sensor fusion technique will be developed.

Monte Carlo simulation of light propagation in healthy and diseased onion bulbs with multiple layers

Cotton quality, a major factor determining both cotton profitability and marketability, is affected by not only the overall quantity of but also the type of the foreign matter. Although current commercial instruments can measure the overall amount of the foreign matter, no instrument can differentiate various types of foreign matter. The goal of this study was to develop a hyperspectral imaging system to discriminate major types of foreign matter in cotton lint. A push-broom based hyperspectral imaging system with a custom-built multi-thread software was developed to acquire hyperspectral images of cotton fiber with 15 types of foreign matter commonly found in the U.S. cotton lint. A total of 450 (30 replicates for each foreign matter) foreign matter samples were cut into 1 by 1 cm2 pieces and imaged on the lint surface using reflectance mode in the spectral range from 400-1000 nm. The mean spectra of the foreign matter and lint were extracted from the user-defined region-of-interests in the hyperspectral images. The principal component analysis was performed on the mean spectra to reduce the feature dimension from the original 256 bands to the top 3 principal components. The score plots of the 3 principal components were used to examine clusterization patterns for classifying the foreign matter. These patterns were further validated by statistical tests. The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint. Nine types of cotton foreign matter formed distinct clusters in the score plots. Additionally, all of them were significantly different from each other at the significance level of 0.05 except brown leaf and bract. The developed hyperspectral imaging system is effective to detect and classify cotton foreign matter on the lint surface and has the potential to be implemented in commercial cotton classing offices.

Blueberry Postharvest Disease Detection Using an Electronic Nose

Light propagation in 18-layer onion bulbs was simulated by Monte Carlo method.18 cases of infected onions demonstrated potential for nondestructive detection.Confident detection was determined with infection as deep as in the 3rd scale.Optimal window for disease detection was 670-870nm especially at 800nm.Spatially-resolved reflectance was effective to detect Neck Rot-infected onions. It remains unanswered how the light interacts with healthy and pathogen-infected onion tissues in a multi-layer structure. The overall goal of this study was to simulate light propagation (including scattering and absorption) in healthy and pathogen-infected onion bulbs in the visible and near infrared (NIR) range using Monte Carlo simulations. Healthy onions and bulbs infected with two major onion post-harvest diseases, Botritis Allii (Neck Rot) and Burkholderia Cepacia (Sour Skin), were considered as the subjects of the simulation. Multi-layered models (18 layers in total) of healthy and infected onion bulbs were developed representing onion structure in the form of parallel slabs. Variance of optical properties was introduced into the models using median and quartile values computed from the experimental data. Monte Carlo-simulations were performed for the developed models to generate optical responses of 33 cases of healthy and infected onions representing different stages of disease propagation in the spectral range 550-1650nm. Optical responses of all the cases were assessed with statistical tests. Study of spatially-resolved scattering reflectance was conducted to identify patterns typical for infected onions. Optical responses were measured experimentally to validate the simulation results for healthy onions. A total of 18 configurations (out of 33) of infected onions showed significant difference from healthy bulbs and demonstrated great potential for nondestructive detection. Confident detection was determined for onions with infection as deep as in the 3rd scale. The proposed optimal window for disease detection was 670-870nm. The greatest discrepancy between optical response of infected and healthy onions was found at 800nm. Spatially-resolved reflectance of the Neck Rot-infected onions showed consistent lower intensity than that of healthy onions over the entire studied radial range, whereas the Sour Skin-infected onions exhibited differences in a limited radial range. Light penetration simulation revealed that photons can reach 5-6mm deep in the bulb in the case of one dry skin in the wavelength of around 800nm and 1100nm. Validation results suggested that although the overall pattern of the simulated results and experimental measurements was similar, the systematic error was likely caused by the curvature of the onion bulb and the measurement instrument. This study was the first attempt to use Monte Carlo simulations in the field of post-harvest research to model complex tissues of vegetables using more than 2 layers. The results of the simulation could be useful in developing non-destructive optical sensing methods for onions.

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

In the United States, cultivated blueberries are second only to strawberries as one of the most important berries. In Georgia, the blueberry industry has grown by 170% in economic value between 2000 and 2005 and has become Georgia’s most important fruit crop with a total farm gate value exceeding