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A hyperspectral microscope imaging (HMI) method, which provides both spatial and spectral information of bacterial cells, was developed for foodborne pathogen detection. The AOTF-basedhyperspectral microscope imaging system can be effective for characterizing spectral properties of biofilms formed by Salmonella enteritidis as well as Escherichia coli. The intensity of spectral images and the pattern of intensity distribution of spectra varied with system parameters (integration time as well as gain) of the HMI system. Preliminary results demonstrated that determination of optimum parameter values of the HMI system and the integration time, which must be no more than 250 ms, are important for quality image acquisition from biofilms formed by S. enteritidis. Among the 89 contiguous spectral images between 450 and 800 nm, the intensity of images at 458, 494, 522, 550, 574, 590, and 670 nm were distinctive for biofilm o/S. enteritidis, whereas the intensity of spectral images at 546 nm was distinctive for E. coli with dark-field illumination with a metal halide light source. For more accurate comparison of intensity from spectral images, a calibration protocol of quantitative intensity comparison needs to be developed to standardize image acquisition using neutral-density filters and multiple exposures. For the identification or classification of unknown foodborne pathogen samples, ground truth region-of-interest pixels need to be selected for "spectrally pure fingerprints 'from various foodborne pathogens such as E. coli and Salmonella species. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) spectroscopy was used to measure the moisture content (MC) of in-shell Virginia and Valencia type peanuts. NIR spectral data from 400 to 2500 nm were collected on calibration samples from Virginia type peanuts ranging in moisture from 6% to 22%. Calibration models to estimate MC were developed using raw and derivative data, and an appropriate model was selected based on parameters such as standard error of calibration and prediction. The ability to predict MC in different types of in-shell peanuts with a single calibration model would save time and labor, otherwise needed for multiple calibrations, and could be useful in developing a commercial instrument. [ABSTRACT FROM AUTHOR]

Peanuts are indeterminate crops and do not mature evenly. Thus, it is difficult to decide the optimal time of harvest. Pod maturity is currently determined by the hull scrape method in conjunction with the maturity profile board (MPB) for estimating the "days until digging". The method is based on the known correlation between maturity level and pod mesocarp color, which is a subjective assessment. The objective of this research was to develop visible (Vis; 400 to 750 nm) and/or visible plus shortwave near-infrared (Vis/NIR; 400 to 1100 nm) reflectance models to predict maturity classes on a pod-by-pod basis. This would allow estimation of the optimum days to dig the crop. Peanuts ('Georgia Green') were harvested on five dates in 2008 and analyzed by the hull scrape method and Vis/NIR reflectance spectroscopy. Spectra from the side of the pod basal segment (N = 754) and from the saddle of the dorsal segment (N = 625) of each pod were analyzed in the Vis and Vis/NIR regions. Partial least squares regression was used to regress MPB maturity column numbers on spectra of pods from three sampling dates. Calibration regression error for MPB class column number was higher for saddle spectra. Based on the calibrations, placement of the pods on the profile board could differ by 1.5 columns, which could alter the estimate of days until digging by ±3 days. Validations of all spectroscopic models were equal to and/or 3 to 7 days longer than the corresponding MPB estimates of days until digging. Separation of the maturity classes was primarily due to the broad and increasing absorbance at 640 nm as the pod matures. [ABSTRACT FROM AUTHOR]

NIR spectroscopy was used to measure the moisture content (MC) of Virginia and Valencia type in-shell peanuts. Peanuts were conditioned to various moisture levels between 7% and 26% (wet basis), and the MC was verified using the standard oven method. Sample from the various moisture levels were separated into two groups, as calibration and validation. NIR absorption spectral data from 400 nm to 2500 nm were collected using peanuts within the calibration and validation sample sets. Measurements were obtained on 30 replicates within each moisture level. Partial least squares (PLS) analysis was performed on the calibration set, and models were developed using the raw spectral data and its derivative function data. The standard error of calibration (SEC) and R² of the calibration models were calculated to select the best calibration model for each peanut market type. Both Valencia and Virginia types gave an R² value of 0.99 for the derivative spectral data treatment as well as for the raw data. The selected models were used to predict the moisture content of peanuts in the validation sample set. Predicted and reference moisture contents were compared. Relative percent deviation (RPD) and standard error of prediction (SEP) were calculated to validate the goodness of fit of the prediction model The raw reflectunce spectra model gave an RPD of 5.55 with a corresponding SEP of O.97 for Valencia type peanuts, which is an indicator that the model is good for quality control and analysis. For Virginia type peanuts, the derivative reflectance spectra model gave the highest RPD value of 5. 75 and the lowest SEP of O. 771. Thus, these two models were selected for the respective peanut types as the best models for prediction of moisture content. [ABSTRACT FROM AUTHOR]