This PDF file contains the front matter associated with SPIE Proceedings Volume 9108, including the Title Page, Copyright information, Table of Contents, Invited Panel Discussion, and Conference Committee listing.

Phage-based magnetoelastic (ME) biosensors have proven useful in rapidly and inexpensively detecting food surface con- tamination. These biosensors are wireless, mass-sensitive biosensors and can be placed directly on food surfaces to detect the presence of target pathogens. Previously, millimeter-scale strip-shaped ME biosensors have been used to demonstrate direct detection of Salmonella Typhimurium on various fresh produce surfaces, including tomatoes, shell eggs, watermel- ons, and spinach leaves. Since the topography of these produce surfaces are different, and the biosensor must come into direct contact with Salmonella bacteria, food surfaces with large roughness and curvatures (e.g., spinach leaf surfaces) may allow the bacteria to avoid direct contact, thereby avoiding detection. The primary objective of this paper is, hence, to investigate the effects of food surface topography on the detection capabilities of the biosensors. Spinach leaf surfaces were selected as model surfaces, and detection experiments were conducted with differently sized biosensors (2 mm, 0.5 mm, and 150 μm in length). Spinach leaf roughness and curvatures of both adaxial (top) and abaxial (underside) surfaces were measured using a confocal laser scanning microscope. The experimental results showed that in spinach as the sen- sor was made smaller, the physical contact between the biosensors and bacteria were improved. Smaller sensors thereby enhance detection capabilities. When proper numbers of biosensors are used, micron-scale biosensors are anticipated to yield improved limits of detection over previously investigated millimeter-scale biosensors.

An electrical circuit was designed and tested to measure the resonant frequency of micron-scale magnetoelastic (ME) biosensors using a pulsed wave excitation technique. In this circuit, a square pulse current is applied to an excitation coil to excite the vibration of ME biosensors and a pick-up coil is used to sense the ME biosensor’s mechanical vibration and convert it to an electrical output signal. The output signal is filtered and amplified by a custom designed circuit to allow the measurement of the resonant frequency of the ME biosensor from which the detection of specific pathogens can be made. As a proof-in-concept experiment, JRB7 phage-coated ME biosensors were used to detect different concentrations of Bacillus anthracis Sterne strain spores. A statistically significant difference was observed for concentrations of 5 × 10² spore/ml and above.

This paper presents the concept of self-propelled magnetoelastic (ME) biosentinels that seek out and capture pathogenic bacteria in stagnant liquids. These biosentinels are composed of a free-standing, asymmetric-shaped ME resonator coated with a filamentous landscape phage that specifically binds with a pathogen of interest. When a time-varying magnetic pulse is applied, the ME biosentinels can be placed into mechanical resonance by magnetostriction. The resultant asymmetric vibration then generates a net force on the surroundings and hence generates autonomous motion in the liquid. As soon as the biosentinels find and bind with the target pathogen through the phage-based biomolecular recognition, a change in the biosentinel’s resonant frequency occurs, and thereby the presence of the target pathogen can be detected. In order to actuate the ME biosentinels into mechanical resonance of a desired mode, modal analysis using the three-dimensional finite element method was performed. In addition, the design of a magnetic chamber that can control the orientation and/or translation of a biosentinel is discussed.

The production of contaminant free fresh fruit and vegetables is needed to reduce foodborne illnesses and related costs. Leafy greens grown in the field can be susceptible to fecal matter contamination from uncontrolled livestock and wild animals entering the field. Pathogenic bacteria can be transferred via fecal matter and several outbreaks of E.coli O157:H7 have been associated with the consumption of leafy greens. This study examines the use of hyperspectral fluorescence imaging coupled with multivariate image analysis to detect fecal contamination on Spinach leaves (Spinacia oleracea). Hyperspectral fluorescence images from 464 to 800 nm were captured; ultraviolet excitation was supplied by two LED-based line light sources at 370 nm. Key wavelengths and algorithms useful for a contaminant screening optical imaging device were identified and developed, respectively. A non-invasive screening device has the potential to reduce the harmful consequences of foodborne illnesses.

Glyphosate based herbicide programs are most preferred in current row crop weed control practices. With the increased use of glyphosate, weeds, including Italian ryegrass (Lolium multiflorum), have developed resistance to glyphosate. The identification of glyphosate resistant weeds in crop fields is critical because they must be controlled before they reduce the crop yield. Conventionally, the method for the identification with whole plant or leaf segment/disc shikimate assays is tedious and labor-intensive. In this research, we investigated the use of high spatial resolution hyperspectral imagery to extract spectral curves derived from the whole plant of Italian ryegrass to determine if the plant is glyphosate resistant (GR) or glyphosate sensitive (GS), which provides a way for rapid, non-contact measurement for differentiation between GR and GS weeds for effective site-specific weed management. The data set consists of 226 greenhouse grown plants (119 GR, 107 GS), which were imaged at three and four weeks after emergence. In image preprocessing, the spectral curves are normalized to remove lighting artifacts caused by height variation in the plants. In image analysis, a subset of hyperspectral bands is chosen using a forward selection algorithm to optimize the area under the receiver operating characteristic (ROC) between GR and GS plants. Then, the dimensionality of selected bands is reduced using linear discriminant analysis (LDA). Finally, the maximum likelihood classification was conducted for plant sample differentiation. The results show that the overall classification accuracy is between 75% and 80% depending on the age of the plants. Further refinement of the described methodology is needed to correlate better with plant age.

A line-scan hyperspectral system was developed to enable Raman chemical imaging for large sample areas. A custom-designed 785 nm line-laser based on a scanning mirror serves as an excitation source. A 45° dichroic beamsplitter reflects the laser light to form a 24 cm x 1 mm excitation line normally incident on the sample surface. Raman signals along the laser line are collected by a detection module consisting of a dispersive imaging spectrograph and a CCD camera. A hypercube is accumulated line by line as a motorized table moves the samples transversely through the laser line. The system covers a Raman shift range of -648.7-2889.0 cm^-1 and a 23 cm wide area. An example application, for authenticating milk powder, was presented to demonstrate the system performance. In four minutes, the system acquired a 512x110x1024 hypercube (56,320 spectra) from four 47-mm-diameter Petri dishes containing four powder samples. Chemical images were created for detecting two adulterants (melamine and dicyandiamide) that had been mixed into the milk powder.

In 2011 Escherichia, Listeria, and Salmonella species infected over 1.2 million people in the United States, resulting in over 23,000 hospitalizations and 650 deaths. In January 2013 President Obama signed into law the Food and Drug Administration (FDA) Food Safety Modernization Act (FSMA), which requires constant microbial testing of food processing equipment and food to minimize contamination and distribution of food tainted with pathogens. The challenge to preventing distribution and consumption of contaminated foods lies in the fact that just a few bacterial cells can rapidly multiply to millions, reaching infectious doses within a few days. Unfortunately, current methods used to detect these few cells rely on similar growth steps to multiply the cells to the point of detection, which also takes a few days. Consequently, there is a critical need for an analyzer that can rapidly extract and detect foodborne pathogens at 1000 colony forming units per gram of food in 1-2 hours (not days), and with a specificity that differentiates from indigenous microflora, so that false alarms are eliminated. In an effort to meet this need, we have been developing an assay that extracts such pathogens from food, selectively binds these pathogens, and produces surface-enhanced Raman spectra (SERS) when read by a Raman analyzer. Here we present SERS measurements of these pathogens in actual food samples using this assay.

For a cylinder of jamonable muscle of radius R and length much greater than R; considering that the internal resistance to the transfer of water is much greater than the external and that the internal resistance is one certain function of the distance to the axis; the distribution of the punctual moisture in the jamonable cylinder is analytically computed in terms of the Bessel’s functions. During the process of drying and salted the jamonable cylinder is sensitive to contaminate with bacterium and protozoa that come from the environment. An analytical model of contamination is presents using the diffusion equation with sources and sinks, which is solve by the method of the Laplace transform, the Bromwich integral, the residue theorem and some special functions like Bessel and Heun. The critical times intervals of drying and salted are computed in order to obtain the minimum possible contamination. It is assumed that both external moisture and contaminants decrease exponentially with time. Contaminants profiles are plotted and discussed some possible techniques of contaminants detection. All computations are executed using Computer Algebra, specifically Maple. It is said that the results are important for the food industry and it is suggested some future research lines.

A food freezing model is analyzed analytically. The model is based on the heat diffusion equation in the case of cylindrical shaped food frozen by liquid nitrogen; and assuming that the thermal conductivity of the cylindrical food is radially modulated. The model is solved using the Laplace transform method, the Bromwich theorem, and the residue theorem. The temperature profile in the cylindrical food is presented as an infinite series of special functions. All the required computations are performed with computer algebra software, specifically Maple. Using the numeric values of the thermal and geometric parameters for the cylindrical food, as well as the thermal parameters of the liquid nitrogen freezing system, the temporal evolution of the temperature in different regions in the interior of the cylindrical food is presented both analytically and graphically. The duration of the liquid nitrogen freezing process to achieve the specified effect on the cylindrical food is computed. The analytical results are expected to be of importance in food engineering and cooking engineering. As a future research line, the formulation and solution of freezing models with thermal memory is proposed.

A potato’s thermal processing model is solved analytically. The model is formulated using the equation of heat diffusion in the case of a spherical potato processed in a furnace, and assuming that the potato’s thermal conductivity is radially modulated. The model is solved using the method of the Laplace transform, applying Bromwich Integral and Residue Theorem. The temperatures’ profile in the potato is presented as an infinite series of Heun functions. All computations are performed with computer algebra software, specifically Maple. Using the numerical values of the thermal parameters of the potato and geometric and thermal parameters of the processing furnace, the time evolution of the temperatures in different regions inside the potato are presented analytically and graphically. The duration of thermal processing in order to achieve a specified effect on the potato is computed. It is expected that the obtained analytical results will be important in food engineering and cooking engineering.

A mathematical model for drying potato cylinders using solar radiation is proposed and solved analytically. The model incorporates the energy balance for the heat capacity of the potato, the radiation heat transfer from the potato toward the drying chamber and the solar radiation absorbed by the potato during the drying process. Potato cylinders are assumed to exhibit a thermal conductivity which is radially modulated. The method of the Laplace transform, with integral Bromwich and residue theorem will be applied and the analytic solutions for the temperature profiles in the potato cylinder will be derived in the form of an infinite series of Bessel functions, when the thermal conductivity is constant; and in the form of an infinite series of Heun functions, when the thermal conductivity has a linear radial modulation. All computations are performed using computer algebra, specifically Maple. It is expected that the analytical results obtained will be useful in food engineering and industry. Our results suggest some lines for future investigations such as the adoption of more general forms of radial modulation for the thermal conductivity of potato cylinders; and possible applications of other computer algebra software such as Maxima and Mathematica.

Different chemicals are sprayed in fruits and vegetables before and after harvest for better yield and longer shelf-life of crops. Cases of pesticide poisoning to human health are regularly reported due to excessive application of such chemicals for greater economic benefit. Different analytical technologies exist to detect trace amount of pesticides in fruits and vegetables, but are expensive, sample destructive, and require longer processing time. This study explores the application of Raman spectroscopy for rapid and non-destructive detection of pesticide residue in agricultural products. Raman spectroscopy with laser module of 785 nm was used to collect Raman spectral information from the surface of Gala apples contaminated with different concentrations of commercially available organophosphorous (48% chlorpyrifos) pesticide. Apples within 15 days of harvest from same orchard were used in this study. The Raman spectral signal was processed by Savitzky-Golay (SG) filter for noise removal, Multiplicative Scatter Correction (MSC) for drift removal and finally polynomial fitting was used to eliminate the fluorescence background. The Raman spectral peak at 677 cm^-1 was recognized as Raman fingerprint of chlorpyrifos. Presence of Raman peak at 677 cm^-1 after fluorescence background removal was used to develop classification model (presence and absence of pesticide). The peak intensity was correlated with actual pesticide concentration obtained using Gas Chromatography and MLR prediction model was developed with correlation coefficient of calibration and validation of 0.86 and 0.81 respectively. Result shows that Raman spectroscopy is a promising tool for rapid, real-time and non-destructive detection of pesticide residue in agro-products.

Beef marbling is one of the most important indices to assess beef quality. Beef marbling is graded by the measurement of the fat distribution density in the rib-eye region. However quality grades of beef in most of the beef slaughtering houses and businesses depend on trainees using their visual senses or comparing the beef slice to the Chinese standard sample cards. Manual grading demands not only great labor but it also lacks objectivity and accuracy. Aiming at the necessity of beef slaughtering houses and businesses, a beef marbling detection instrument was designed. The instrument employs Charge-coupled Device (CCD) imaging techniques, digital image processing, Digital Signal Processor (DSP) control and processing techniques and Liquid Crystal Display (LCD) screen display techniques. The TMS320DM642 digital signal processor of Texas Instruments (TI) is the core that combines high-speed data processing capabilities and real-time processing features. All processes such as image acquisition, data transmission, image processing algorithms and display were implemented on this instrument for a quick, efficient, and non-invasive detection of beef marbling. Structure of the system, working principle, hardware and software are introduced in detail. The device is compact and easy to transport. The instrument can determine the grade of beef marbling reliably and correctly.

Quality attributes of fresh meat influence nutritional value and consumers' purchasing power. In order to meet the demand of inspection department for portable device, a rapid and nondestructive detection device for fresh meat quality based on ARM (Advanced RISC Machines) processor and VIS/NIR technology was designed. Working principal, hardware composition, software system and functional test were introduced. Hardware system consisted of ARM processing unit, light source unit, detection probe unit, spectral data acquisition unit, LCD (Liquid Crystal Display) touch screen display unit, power unit and the cooling unit. Linux operating system and quality parameters acquisition processing application were designed. This system has realized collecting spectral signal, storing, displaying and processing as integration with the weight of 3.5 kg. 40 pieces of beef were used in experiment to validate the stability and reliability. The results indicated that prediction model developed using PLSR method using SNV as pre-processing method had good performance, with the correlation coefficient of 0.90 and root mean square error of 1.56 for validation set for L*, 0.95 and 1.74 for a*，0.94 and 0.59 for b*, 0.88 and 0.13 for pH, 0.79 and 12.46 for tenderness, 0.89 and 0.91 for water content, respectively. The experimental result shows that this device can be a useful tool for detecting quality of meat.

Pork is one of the highly consumed meat item in the world. With growing improvement of living standard, concerned stakeholders including consumers and regulatory body pay more attention to comprehensive quality of fresh pork. Different analytical-laboratory based technologies exist to determine quality attributes of pork. However, none of the technologies are able to meet industrial desire of rapid and non-destructive technological development. Current study used optical instrument as a rapid and non-destructive tool to classify 24 h-aged pork longissimus dorsi samples into three kinds of meat (PSE, Normal and DFD), on the basis of color L* and pH₂₄. Total of 66 samples were used in the experiment. Optical system based on Vis/NIR spectral acquisition system (300-1100 nm) was self- developed in laboratory to acquire spectral signal of pork samples. Median smoothing filter (M-filter) and multiplication scatter correction (MSC) was used to remove spectral noise and signal drift. Support vector machine (SVM) prediction model was developed to classify the samples based on their comprehensive qualities. The results showed that the classification model is highly correlated with the actual quality parameters with classification accuracy more than 85%. The system developed in this study being simple and easy to use, results being promising, the system can be used in meat processing industry for real time, non-destructive and rapid detection of pork qualities in future.

The viable counts in chicken have significant effects on food safety. Exceeding standard index can have negative influence to the public. Visible-near infrared spectra have had rapid development in food safety recently. The objective of this study was to detect the total viable counts in chicken breast fillets.36 chicken breast fillets used in the study were stored in a refrigerator at 4°C for 9 days. Each day four samples were taken and Vis/NIR spectra were collected from each sample before detecting their total viable counts by standard method. The original data was processed in four main steps: Savitzky-Golay smoothing method, standard normalized variate (SNV), model calibrating and model validating. Prediction model was established using partial least squares regression (PLSR) method. Several statistical indicators such as root mean squared errors and coefficients were calculated for determination of calibration and validation accuracy respectively. As a result, the Rc, SEC, Rv and SEV, of the best model were obtained to be 0.8854, 0.7455, 0.9070 and 0.6045 respectively, which demonstrate that visible-near infrared spectra is a potential technique to detect the total viable counts(TVC) in chicken and the best wavelengths for the establishment of the calibration model are near 449nm.

One of the key issues in developing Good Agricultural Practices (GAP) is represented by the optimal utilisation of fertilisers and herbicidal to reduce the impact of Nitrates in soils and the environment. In traditional agriculture practises, these substances were provided to the soils through the use of chemical products (inorganic/organic fertilizers, soil improvers/conditioners, etc.), usually associated to several major environmental problems, such as: water pollution and contamination, fertilizer dependency, soil acidification, trace mineral depletion, over-fertilization, high energy consumption, contribution to climate change, impacts on mycorrhizas, lack of long-term sustainability, etc. For this reason, the agricultural market is more and more interested in the utilisation of organic fertilisers and soil improvers. Among organic fertilizers, there is an emerging interest for the digestate, a sub-product resulting from anaerobic digestion (AD) processes. Several studies confirm the high properties of digestate if used as organic fertilizer and soil improver/conditioner. Digestate, in fact, is somehow similar to compost: AD converts a major part of organic nitrogen to ammonia, which is then directly available to plants as nitrogen. In this paper, new analytical tools, based on HyperSpectral Imaging (HSI) sensing devices, and related detection architectures, is presented and discussed in order to define and apply simple to use, reliable, robust and low cost strategies finalised to define and implement innovative smart detection engines for digestate characterization and monitoring. This approach is finalized to utilize this “waste product” as a valuable organic fertilizer and soil conditioner, in a reduced impact and an “ad hoc” soil fertilisation perspective. Furthermore, the possibility to contemporary utilize the HSI approach to realize a real time physicalchemical characterisation of agricultural soils (i.e. nitrogen, phosphorus, etc., detection) could allow to set up “real time” selective fertilization strategies in order to obtain a safer culture production.