Yufeng Ge

Spectral data mining for rapid measurement of organic matter in unsieved moist compost

Fifty-five compost samples were collected and scanned as received by visible and near-IR (VisNIR, 350-2500 nm) diffuse reflectance spectroscopy. The raw reflectance and first-derivative spectra were used to predict log(10)-transformed organic matter (OM) using partial least squares (PLS) regression, penalized spline regression (PSR), and boosted regression trees (BRTs). Incorporating compost pH, moisture percentage, and electrical conductivity as auxiliary predictors along with reflectance, both PLS and PSR models showed comparable cross-validation r(2) and validation root-mean-square deviation (RMSD). The BRT-reflectance model exhibited best predictability (residual prediction deviation=1.61, cross-validation r(2)=0.65, and RMSD=0.09 log(10)%). These results proved that the VisNIR-BRT model, along with easy-to-measure auxiliary variables, has the potential to quantify compost OM with reasonable accuracy.

Toward On-line Measurement of Algal Properties

Algae is a potential source of large amounts of lipids for conversion to hydrocarbon fuels. Industrial-scale algae production requires process control, which further requires sensors to measure critical algal properties. One of the principal properties that needs to be measured in algae production is optical density. In this work an opto-electronic sensor was developed for the initial purpose of measuring optical density in real time in situ. A prototype was built and proved to be very accurate, with an R2 value greater than 0.98 when compared to laboratory OD measurements. The prototype also worked very well in a field test in which the range of OD values measured was limited.

Improvement of an Optical Density Sensor for Algae Pond Monitoring and Process Control

The objectives of this study are to (1) improve a previously developed optical density (OD) sensor for the measurement of biomass concentration in algae cultures, and (2) test the performance of the improved sensor. The sensor was improved in the following several aspects. First, the sensor housing was redesign to accommodate new optical measurement configuration and a reference cell. Second, a constant current LED driver circuit was built and included. Third, a feedback temperature control mechanism (including thermistors and thermo-electrical cooling modules) was built to control the temperature of LEDs. Finally, a logarithmic IC chip was used for processing of raw outputs from photodiodes. The new sensor was tested with a 40 L algae cultivation raceway for seven days. The sensor showed high accuracy (R2 = 0.98 and root mean squared error = 0.034 absorbance unit between sensor-predicted and spectroscopically-determined OD). The sensor responded to the long-term growth of algae (i.e., the alternate growth patterns in light-on and light-off cycles) very well. The sensor also responded to cultivation events including water and media addition and culture transfer very well. The temperature dependency of the new sensor was 0.0033 and 0.0039 V/°C for the NIR and red channel, respectively, compared to 0.0137 and 0.08 V/°C for the old sensor.

Algal Lipid Quantification in-situ with Nile Red Fluorescence

Microalgae have recently garnered substantial interests for biofuel production. The overall goal of this research is to develop an optical sensor based on Nile Red fluorescence (NRf) that can quantify algal neutral lipids real time in situ. The specific objective of this study is to elucidate the effect of several important parameters on NRf signal intensity including emission maxima, temperature, staining time, and algal species. Three algae species, Nannochloropsis Oculata, Nannochloropsis Salina, and Botryococcus braunii (Race A and B), were used. A spectrofluorometer was used to identify NRf emission maxima and investigate the temperature effect; and a single-band fluorometer was used to investigate the effect of staining time and species. For all algae types, the NRf emission maximum is at 590 nm. Temperature has a large impact, with NRf intensity increasing almost proportionally with temperature. NRf signal increases from minute 0 to 4 after staining. Finally, NRf intensity is linearly correlated with the neutral lipid content in algae culture (using optical density of the culture as a proxy, R2=0.96 and 0.88 for Nanno. and B. braunii, respectively) but the relationship is dependent on species.

Wireless GPS System for Module-level Fiber Quality Mapping: System Improvement and Field Testing

The ability to map the profit made across a cotton field would enable producers to see in detail where money is being made or lost on their farm. This ability would further enable them to implement precise field management practices to ensure that they receive the highest return possible and do not waste materials and other inputs throughout the field. Investigators at Texas A&M have developed a wireless-GPS system that tracks where a module of cotton comes from within a field. This system is a necessary component in mapping fiber quality, which is a major determiner of price and thus profit. Three current drawbacks to the wireless-GPS system are that (1) a person must manually trigger the system to send wireless communications when a field machine dumps, (2) multiple field machines of the same type (e.g., two cotton pickers) cannot be used simultaneously on the same system within the same field, and (3) no software is available to automatically produce fiber-quality maps after the data are downloaded from the gin. An automatic communication-triggering system is the problem to be addressed in this work. Sensors are being added to a harvester to automatically indicate when the machine is dumping a basket load of cotton so that wireless messages can be sent from the harvester to subsequent field machines without human intervention. Linking data collected with this system together with classing information will enable producers to create fiber-quality maps, and linking fiber-quality maps with yield maps will enable them to create profit maps.

New technologies in Field Soil Survey

Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) and field portable x-ray fluorescence spectrometry were used to quantify soil parameters on site. Their operational theory and application to soil science are presented.

Multispectral Sensor for In-Situ Cotton Fiber Quality Measurement

Reflectance spectra of cotton fiber samples having different fiber quality levels were measured with a high-resolution spectrophotometer. Reflectance spectra of the cotton samples were processed with waveband averaging and wavelet analysis, and then related to micronaire by using multiple linear regression. Regression models indicated that the micronaire had a strong correlation (r2 = 0.89) with the reflectance values at seven 100-nm wavebands (1120, 1296, 1550, 1664, 1852, 2020, and 2340 nm). In wavelet analysis, six wavelet regressors were identified and entered into the regression model. These models also indicated a very strong correlation between micronaire and reflectance spectra in the wavelet domain (r2 = 0.97). A prototype of cotton fiber quality sensor was developed based on the characteristics of the cotton fiber reflectance spectrum. The sensor consists of a VisGaAs camera, optical bandpass filters, halogen light source, and an image collection and process system. The sensor was tested in the laboratory conditions. Images of lint samples at three near infrared (NIR) wavebands (1450, 1550, and 1600 nm) were acquired and analyzed to determine the relationship between the image pixel value and cotton fiber micronaire. Results showed that the sensor was capable of accurately assessing the fiber micronaire (r2 = 0.99). This sensor could be used for measuring cotton fiber quality along with their corresponding spatial data from GPS as cotton is harvested in fields, which makes it possible to generate cotton fiber quality maps. It also has the potential being used for segregating cotton based on fiber quality during harvesting.