Kangjin Lee

NEAR-INFRARED DIFFUSE REFLECTANCE FOR QUANTITATIVE AND QUALITATIVE MEASUREMENT OF SOLUBLE SOLIDS AND FIRMNESS OF DELICIOUS AND GALA APPLES

Development of nondestructive measurements of soluble solids and firmness of apples benefits the producers, processors, and packers. The soluble solids and firmness (Magness-Taylor (MT) maximum force) of apples were predicted by diffuse reflectance measurement in the visible/near-infrared (400-1800 nm) regions of the spectrum. Two apple cultivars, Gala (n = 800) and Red Delicious (n = 960), were measured. The spectroscopic measurements of soluble solids and firmness for each apple were validated by a refractometer reading on the juice and a puncture test using a universal testing instrument, respectively. The soluble solids of apples could be predicted by NIR spectroscopic techniques with principal component regression (PCR) and Mahalanobis Distance (MD) analysis. The coefficients of determination (R2) for predicting soluble solids were 0.934 (SEP = 0.279; 10 factors) for Gala and 0.966 (SEP = 0.341; 10 factors) for Red Delicious apples with an NIR spectrum range (800-1100 nm). For classifying Gala apples into three classes based on the soluble solids, MD classifiers had classification accuracies of 93.5% for the full spectrum range (400-1800 nm) and 95.5% for the partial NIR spectrum range (800-1100 nm). Similarly, for the classification of Red Delicious apples, the classification accuracies were 92.1% with the full spectrum (400-1800 nm) and 93.6% with the partial NIR spectrum (800-1100 nm). A spectroscopic technique for apple firmness measurements was feasible for the Delicious apple. Using PCR models, the R2 values for predicting MT firmness were 0.218 (SEP = 4.91; 16 factors) for Gala and 0.786 (SEP = 7.02; 24 factors) for Red Delicious apples with the full spectrum (400-1800 nm), while the R2 values were only 0.291 (SEP = 4.92; 12 factors) for Gala and 0.657 for Red Delicious (SEP = 7.33; 24 factors) with the partial NIR spectrum (800-1100 nm). The MD analyses were also conducted to classify apples firmness. Based on three classes, the classification accuracies were 82.5% with the full spectrum (400- 1800 nm) and 80% with the partial NIR spectrum (800-1100 nm) for Gala apples. Similarly, the classification accuracies were 83.8% with the full spectrum (400-1800 nm) and 75.3% with the partial NIR spectrum (800-1100 nm) for Red Delicious apples. This shows that spectroscopic techniques are feasible to classify apple firmness with over 80% accuracy.

Line-Scan Hyperspectral Imaging Platform for Agro-Food Safety and Quality Evaluation: System Enhancement and Characterization

Line-scan-based hyperspectral imaging techniques have often served as a research tool to develop rapid multispectral methods based on only a few spectral bands for rapid online applications. With continuing technological advances and greater accessibility to and availability of optoelectronic imaging sensors and spectral imaging spectrographs, the range of implementation for hyperspectral imaging has been broadening across quality and safety inspection needs in the food and agricultural industries. We have developed a series of food inspection imaging systems based on hyperspectral line-scan imaging with the use of a low-light sensitive, electron-multiplying charge-coupled device (EMCCD). In this methodology article, the spectral and spatial system performance of the latest generation of the ARS hyperspectral imaging system, which is capable of reflectance and fluorescence measurements in the visible and near-infrared (NIR) spectral regions from 400 to 1000 nm, is evaluated. Results show that the spectral resolution of the system is 4.4 nm at full-width at half-maximum (FWHM) and 6 nm FWHM at our typical operation mode (6-pixel spectral binning). We enhanced the system throughput responses by using spectral weighting filters to better utilize the dynamic range of the analog-to-digital converter. With this system throughput adjustment, noise-equivalent reflectance measurements were significantly reduced by approximately 50% in the NIR region for a range of standard diffuse reflectance targets. The responsivity of the system from 450 to 950 nm was determined to be linear.

Enhanced Raman spectroscopic discrimination of the geographical origins of rice samples via transmission spectral collection through packed grains.

Transmission Raman spectroscopy has been effectively utilized for the discrimination of rice samples according to geographical origin. Since the constituents of rice are heterogeneously distributed and/or localized in a grain, the collection of Raman spectra providing a better compositional representation of packed rice grains is an essential requirement for accurate analysis. The optimal packing thickness yielding the most reproducible transmission spectra was initially determined. Internal propagation of radiation was more sensitively influenced by random packing when a packing was thinner; while, a thicker packing largely attenuated transmitting Raman signal and eventually degraded the signal-to-noise ratio of collected spectra. At the determined packing thickness, transmission spectra of all rice samples were collected, and discrimination into two different geographical origins was performed using principal component analysis (PCA) combined with linear discriminant analysis (LDA). For comparison, back-scattering Raman spectra of the same samples were also collected. The discrimination accuracy was improved when Raman spectra collected directly through the packed rice grains were used. Since the constituents of rice were not homogeneously distributed in a grain as confirmed using Raman microscopy, the transmission measurement enabling transversal sampling across a packing of rice grains was better for compositional representation of individual grains in the packing and able to recognize minute spectral differences between two groups, ultimately leading to more accurate discrimination of geographical origin.

Hyperspectral Imaging for Detecting Defect on Apples

The reflectance spectra from the hyperspectral images of apples were used to find the optimal wavelengths to discriminate the defect region from the normal region. The optimal wavelength was selected from the correlation analysis between the band ratio or wavelength difference and target regions. The spectral images of selected wavelength were used to validate the correlation analysis. The correlation coefficient value using the band ratio at correlation analysis was 0.91 and the using the difference was 0.79. Thus, the correlation analysis method is feasible to select the wavelength to discriminate defects from normal regions.

Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging

In this study, we developed a viability evaluation method for pepper (Capsicum annuum L.) seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB), which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm) yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm) yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Non-destructive and Rapid Prediction of Moisture Content in Red Pepper (Capsicum annuum L.) Powder Using Near-infrared Spectroscopy and a Partial Least Squares Regression Model

National Academy of Agricultural Science, Rural Development Administration, 150 Suinro, Gwonseon-gu, Suwon, Gyeonggi-do 441-100, Korea Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, 10300 Baltimore Ave, Beltsville, MD 20705 Received: July 23rd 2014; Revised: July 29th 2014; Accepted: August 9th 2014 Purpose: The aim of this study was to develop a technique for the non-destructive and rapid prediction of the moisture content in red pepper powder using near-infrared (NIR) spectroscopy and a partial least squares regression (PLSR) model. Methods: Three red pepper powder products were separated into three groups based on their particle sizes using a standard sieve. Each product was prepared, and the expected moisture content range was divided into six or seven levels from 3 to 21% wb with 3% wb intervals. The NIR reflectance spectra acquired in the wavelength range from 1,100 to 2,300 nm were used for the development of prediction models of the moisture content in red pepper powder. Results: The values of RV2, SEP, and RPD for the best PLSR model to predict the moisture content in red pepper powders of varying particle sizes below 1.4 mm were 0.990, ±0.487% wb, and 10.00, respectively. Conclusions: These results demonstrated that NIR spectroscopy and a PLSR model could be useful techniques for measuring rapidly and non-destructively the moisture content in red pepper powder.

A Near-Infrared Sensing Technique for Measuring the Quality of Potatoes

Near–infrared (NIR) spectroscopy is a promising technique for nondestructive sensing of agricultural product for multiple quality attributes. The objective of this research was to explore a NIR sensing technique in interaction mode for rapid acquisition of spectral information to predict the quality parameters of potatoes. The percentage of dry matter and specific gravity are important quality parameters for assessing the potential of potato tubers to produce acceptable yields of processed products with good textural and color quality attributes. In this research, the NIR calibration for dry matter and specific gravity was developed with an on-line system to predict and classify the specific gravity and dry matter of potatoes. The model predicted specific gravity of intact potatoes with 87% accuracy compared with the measured data, and the Standard Error of Correlation (SEC) was ±0.0022. From the cross validation result, the correlation coefficient was 0.85 and the Standard Error of Prediction (SEP) was ±0.0024. For the dry matter of intact potatoes, the correlation coefficient of calibration was 0.85 and the Standard Error of Correlation (SEC) was ± 0.60%. The correlation coefficient of prediction was 0.82 and the Standard Error of Prediction (SEP) was ±0.66%. Thus, the developed model and the on-line VIS/NIR transmittance system were feasible to classify the potato by the specific gravity and dry matter.

Determination of Germination Quality of Cucumber (Cucumis Sativus) Seed by LED-Induced Hyperspectral Reflectance Imaging

Purpose: We developed a viability evaluation method for cucumber (Cucumis sativus) seed using hyperspectral reflectance imaging. Methods: Reflectance spectra of cucumber seeds in the 400 to 1000 nm range were collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA) was developed to predict viable and non-viable seeds. Various ranges of spectra induced by four types of LEDs (Blue, Green, Red, and RGB) were investigated to develop the classification models. Results: PLS-DA models for spectra in the 600 to 700 nm range showed 98.5% discrimination accuracy for both viable and non-viable seeds. Using images based on the PLS-DA model, the discrimination accuracy for viable and non-viable seeds was 100% and 99%, respectively Conclusions: Hyperspectral reflectance images made using LED light can be used to select high quality cucumber seeds.

On-line internal quality evaluation system for the processing potatoes

Near infrared (NIR) spectroscopy has been used to measure the multiple quality attributes in agricultural product nondestructively. To produce acceptable yields of processed potato products with good textural and color, the dry matter, specific gravity and hollow heart are important quality parameters. The objective of this research was to investigate the possibility of predicting the percentage of specific gravity, and dry matter and detect the hollow heart in potato (Solanum tuberosum L.) tubers by using NIR sensing technique as a rapid and nondestructive method.

Portable hyperspectral fluorescence imaging system for detection of biofilms on stainless steel surfaces

A rapid nondestructive technology is needed to detect bacterial contamination on the surfaces of food processing equipment to reduce public health risks. A portable hyperspectral fluorescence imaging system was used to evaluate potential detection of microbial biofilm on stainless steel typically used in the manufacture of food processing equipment. Stainless steel coupons were immersed in bacterium cultures, such as E. coli, Pseudomonas pertucinogena, Erwinia chrysanthemi, and Listeria innocula. Following a 1-week exposure, biofilm formations were assessed using fluorescence imaging. In addition, the effects on biofilm formation from both tryptic soy broth (TSB) and M9 medium with casamino acids (M9C) were examined. TSB grown cells enhance biofilm production compared with M9C-grown cells. Hyperspectral fluorescence images of the biofilm samples, in response to ultraviolet-A (320 to 400 nm) excitation, were acquired from approximately 416 to 700 nm. Visual evaluation of individual images at emission peak wavelengths in the blue revealed the most contrast between biofilms and stainless steel coupons. Two-band ratios compared with the single-band images increased the contrast between the biofilm forming area and stainless steel coupon surfaces. The 444/588 nm ratio images exhibited the greatest contrast between the biofilm formations and stainless coupon surfaces.