Showing papers in "Sensing and Instrumentation for Food Quality and Safety in 2009"

Theory and application of near infrared spectroscopy in assessment of fruit quality: A review

Abstract: Fruits provide nutrients for human body and are able to prevent sorts of non-communicable diseases. The fruit quality test is an area that both technology and market section concern about. Near infrared spectroscopy (NIR) is a rapid, precise, and non-destructive technique which can be well utilized in determination of fruit quality. This review paper summarizes the theory of NIR analysis, and the fundamental structure of instruments based on NIR for fruit quality assessment. Chemometrics for NIR spectroscopy involving analysis methods of data pre-processing, calibration, model transfer and evaluation, is also included. In recent 11 years, significant progresses were achieved in fruit quality assessment via NIR spectroscopy, which is the main focus in this review. Furthermore, urgent problems in this research field are discussed, expecting to be solved in the near future.

Prediction of white button mushroom ( Agaricus bisporus ) moisture content using hyperspectral imaging

Abstract: Hyperspectral imaging is a non-contact, non-destructive technique that combines spectroscopy and imaging to extract information from a sample. This technology has recently emerged as a powerful technique for food analysis. In this study, the potential of hyperspectral imaging (HSI) to predict white button mushroom moisture content (MC) was investigated. Mushrooms were subjected to dehydration at 45 ± 1 °C for different time periods (0, 30, 60 and 120 min) to obtain representative samples at different moisture levels (93.40 ± 0.62%, 82.76 ± 2.11%, 73.20 ± 2.60% and 60.89 ± 4.32% wet basis [wb]). Hyperspectral images of the mushrooms were obtained using a pushbroom system operating in the wavelength range of 400–1000 nm. Hunter L, a and b colour values of the mushrooms were also measured. The average reflectance spectra of samples at different MC levels were obtained and Partial Least Square Regression (PLSR) models were built to predict mushroom moisture content. To reduce the spectral variability caused by factors unrelated to MC such as scattering effects and differences in sample height, different spectral pre-treatments were applied. The Standard Normal Variate (SNV) transformation was found to be the best approach among the wavelength range studied, resulting in the greatest reduction in Root Mean Square Error of Cross Validation (RMSECV) and Root Mean Square Error of Prediction (RMSEP) for a 4-component PLSR model. RMSECV of 5.50 (% wb) and RMSEP of 5.58 (% wb) were obtained for the calibration and test sets of data, respectively. Prediction maps were generated from hyperspectral data to show the predictive model performance at pixel level. This study shows the potential of hyperspectral imaging for prediction of mushroom moisture content in the studied wavelength range. The implemented method highlighted contrast between areas of different moisture content to achieve better knowledge of dehydration distribution over the mushroom surface.

Detection of soybean rust using a multispectral image sensor

Abstract: Soybean rust, caused by Phakopsora pachyrhizi, is one of the most destructive diseases for soybean production. It often causes significant yield loss and may rapidly spread from field to field through airborne urediniospores. In order to implement timely fungicide treatments for the most effective control of the disease, it is essential to detect the infection and severity of soybean rust. This research explored feasible methods for detecting soybean rust and quantifying severity. In this study, images of soybean leaves with different rust severity were collected using both a portable spectroradiometer and a multispectral CDD camera. Different forms of vegetation indices were used to investigate the possibility of detecting rust infection. Results indicated that both leaf development stage and rust infection severity changed the surface reflectance within a wide band of spectrum. In general, old leaves with most severe rust infection resulted in lowest reflectance. A difference vegetation index (DVI) showed a positive correlation with reflectance differences. However, it lacks solid evidence to identify such reflectance change was solely caused by rust. As an alternative, three parameters, i.e. ratio of infected area (RIA), lesion color index (LCI) and rust severity index (RSI), were extracted from the multispectral images and used to detect leaf infection and severity of infection. The preliminary results obtained from this laboratory-scale research demonstrated that this multispectral imaging method could quantitatively detect soybean rust. Further tests of field scale are needed to verify the effectiveness and reliability of this sensing method to detect and quantify soybean rust infection in real time field scouting.

Using a Short Wavelength Infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels

Abstract: Sprout damage (pre-harvest germination) in wheat results in highly deleterious effects on end-product quality Alpha-amylase, the pre-dominant enzyme in the early stage of sprouting has the most damaging effect This paper introduces a new method using a SWIR hyperspectral imaging system (1000–2500 nm) to predict the α-amylase activity of individual wheat kernels Two classes of Canadian wheat, Canada Western Red Spring (CWRS) and Canada Western Amber Durum (CWAD), with samples of differing degrees of sprout damage were investigated Individual kernels were first imaged with the hyperspectral imaging system and then the α-amylase activity of each kernel was determined analytically Individual kernel α-amylase activity prediction was significant (R 2 054 and 073) for CWAD and CWRS, respectively using Partial Least Square regression on the hyperspectral data A classification method is proposed to separate CWRS kernels with high α-amylase activity level from those with low α-amylase activity giving an accuracy of above 80% This work shows that hyper/multi-spectral imaging techniques can be used for rapidly predicting the α-amylase activity of individual kernels, detecting sprouting at early stage

Assessment of bacterial biofilm on stainless steel by hyperspectral fluorescence imaging

Abstract: Hyperspectral fluorescence imaging techniques were investigated for detection of two genera of microbial biofilms on stainless steel material which is commonly used to manufacture food processing equipment. Stainless steel coupons were deposited in nonpathogenic E. coli O157:H7 and Salmonella cultures, prepared using M9 minimal medium with casamino acids (M9C), for 6 days at 37 °C. Hyperspectral fluorescence emission images of the biofilm formations on the stainless coupons were acquired from 416 to 700 nm with the use of ultraviolet-A (320–400 nm) excitation. In general, emission peaks for both bacteria were observed in the blue region at approximately 480 nm and thus provided the highest contrast between the biofilms and background stainless steel coupons. A simple thresholding of the 480 nm image showed significantly larger biofilm regions for E. coli O157:H7 than for Salmonella. Viable cell counts suggested that Salmonella formed significantly higher density biofilm regions than E. coli O157:H7 in M9C medium. On the basis of principal component analysis (PCA) of the hyperspectral fluorescence images, the second principal component image exhibited the most distinguishable morphological differences for the concentrated biofilm formations between E. coli and Salmonella. E. coli formed granular aggregates of biofilms above the medium on stainless steel while Salmonella formed dense biofilm in the medium-air interface region (pellicle). This investigation demonstrated the feasibility of implementing fluorescence imaging techniques to rapidly screen large surface areas of food processing equipment for bacterial contamination.

Use of spectroscopic data for automation in food processing industry

Abstract: Advances in spectroscopy now enable researchers to obtain information about chemical and physical components in food or biological materials at the molecular level. Various spectroscopic techniques (e.g., atomic absorption spectroscopy, Raman and Fourier-transform infrared spectroscopy, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, mass spectroscopy, X-ray fluorescence spectroscopy, ultra-violet spectroscopy) have been used to study structure-function relationships in foods (both liquid and solid) to improve overall food quality, safety and sensory characteristics; to investigate fungal infections in plant materials (e.g., fruits, seeds); or to study mobility of different chemical components in food materials. Processing, analyzing, and displaying these data can often be difficult, time-consuming, and problem-specific. Chemometrics is well established for calibrating the spectral data to predict concentrations of constituents of interest. Similarly, proteomics deals with the structure-function relationship of proteins. Since most of the food processing industries are becoming increasingly automated, there is a need to understand how the spectroscopic data can be used for automation. In this paper, we have provided basic working principles of the above mentioned spectroscopic techniques, examples of the use of spectral data in food processing, methods of analysis of spectral data and their integration in the automation process.

Machine vision system for online inspection of freshly slaughtered chickens

Abstract: A machine vision system was developed and evaluated for the automation of online inspection to differentiate freshly slaughtered wholesome chickens from systemically diseased chickens. The system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera used with an imaging spectrograph and controlled by a computer to obtain line-scan images quickly on a chicken processing line of a commercial poultry plant. The system scanned chicken carcasses on an eviscerating line operating at a speed of 140 chickens per minute. An algorithm was implemented in the system to automatically recognize individual carcasses entering and exiting the field of view, to locate the region of interest (ROI) of each chicken, to extract useful spectra from the ROI as inputs to the differentiation method, and to determine the condition for each carcass as being wholesome or systemically diseased. The system can acquire either hyperspectral or multispectral images without any cross-system calibration. The essential spectral features were selected from hyperspectral images of chicken samples. The differentiation of chickens on the processing line was then carried out using multispectral imaging. The high accuracy obtained from the evaluation results showed that the machine vision system can be applied successfully to automatic online inspection for chicken processing.

Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing

Abstract: The potential of visible-near infrared spectra, obtained using a light backscatter sensor, in conjunction with chemometrics, to predict curd moisture and whey fat content in a cheese vat was examined. A three-factor (renneting temperature, calcium chloride, cutting time), central composite design was carried out in triplicate. Spectra (300–1,100 nm) of the product in the cheese vat were captured during syneresis using a prototype light backscatter sensor. Stirring followed upon cutting the gel, and samples of curd and whey were removed at 10 min intervals and analyzed for curd moisture and whey fat content. Spectral data were used to develop models for predicting curd moisture and whey fat contents using partial least squares regression. Subjecting the spectral data set to Jack-knifing improved the accuracy of the models. The whey fat models (R = 0.91, 0.95) and curd moisture model (R = 0.86, 0.89) provided good and approximate predictions, respectively. Visible-near infrared spectroscopy was found to have potential for the prediction of important syneresis indices in stirred cheese vats.

Application of near infrared spectroscopy to peanut grading and quality analysis: overview

Abstract: Techniques using near infrared (NIR) spectroscopy for quality measurements are becoming more popular in food processing and quality inspection of agricultural commodities. NIR spectroscopy has several advantages over conventional physical and chemical analytical methods of food quality analysis. It is a rapid and non destructive method and provides more information about the components and its structure present in the food products. It can measure more than one parameter simultaneously. The NIR spectrum includes wavelengths from 750 to 3000 nm that follow immediately after the visible region (400–700 nm). Many organic compounds can be well-defined by NIR reflectance, transmittance or diffuse reflectance system. This paper reviews the application of NIR spectroscopy to several oil seeds and examines the feasibility of using this technique for peanut quality analysis. The NIR spectroscopic instrumentation has been explained briefly for a better understanding. Also needs and limitations in use of NIR spectroscopy for peanut quality analysis and grading were explained.

Processing and packaging automation systems: a review

Abstract: Global food processing and packaging business has reached to multi trillion dollars as the consumers have started using processed food more than the staples. This paper reviews aspects of systems, standards and interfaces for the modern food industry. It presents processing and packaging principles, methods, techniques, standards, interfaces, and state-of-the-art technology. The primary purpose of this paper is to update the information in this domain so that not only the novice practitioner will gain much out of this review but the system integrators, researchers, and the stakeholders as well. In addition, the paper covers recent advances in smart packaging materials, the examples of nanotechnology in packaging, material handling systems, application of robotics, non-destructive inspection methods, packaging execution systems (PES), distributed control and automation systems, traceability, and finally OMAC (Open Modular Architecture Controls) guidelines on software standards and interfaces.

Robotization in fruit grading system

Abstract: A grading robot system, which automatically provides fruits from containers and inspects all sides of the fruits was developed. The robot system consists of two fruit providing robots and a grading robot. Both robots had 3 DOF Cartesian coordinate manipulators, and 12 suction pads as end-effectors for transporting fruits. The grading robot had 12 color TV cameras, and 28 lighting devices as a machine vision system. The grading robot sucked 12 fruits up at a time and 12 bottom images of all the fruits were acquired during the manipulator moving from a halfway stage to carriers on a conveyor line. Before releasing the fruits to the carriers, 4 side images of each fruit were acquired by rotating the suction pads for 270°. The stroke of the manipulators was about 1.2 m and it took about 4.3 s to move back to the initial position that meant that this robot could grade three fruits per second. This grading robot system made following effects: (1) Labor substitution, (2) Objective grading operation without human subjective judgment, (3) Data accumulation on fruit grading for traceability and farming guidance to producer.

Feasibility of colloidal silver SERS for rapid bacterial screening

Abstract: This study reports the feasibility of citrate-reduced colloidal silver surface-enhanced Raman scattering (SERS) for differentiating three important food borne pathogens, E. coli, Listeria, and Salmonella. FT-Raman and SERS spectra of both silver colloids and colloid-K3PO4 mixtures were collected and analyzed to evaluate the reproducibility and stability of silver colloids fabricated in a batch-production process. The results suggest that the reproducibility of the colloids over the batch process is high and that their binding effectiveness remains consistent over a 60-day storage period. Two specific SERS bands at 712 and 390 cm−1 were identified and used to develop simple 2-band ratios for differentiating E. coli-, Listeria-, and Salmonella-colloid mixtures with a 100% success. These results indicate that colloidal silver SERS technique may be a practical alternative method suitable for routine and rapid screening of E. coli, Listeria, and Salmonella bacteria.

Microwave nondestructive sensing of moisture content in shelled peanuts independent of bulk density and with temperature compensation

Abstract: Dielectric methods for rapid and nondestructive sensing of moisture content in shelled peanuts from free-space measurement of attenuation and phase shift, and their corresponding dielectric properties at temperatures ranging from 1 to 38 °C and frequencies ranging from 8 to 14 GHz, are presented. These methods provide moisture content independent of bulk density and compensated for temperature effects. Results of moisture prediction with three density-independent calibration functions (ψ1, ψ2, and ψ3) are compared. For each function, the moisture calibration equation with temperature compensation is given along with corresponding standard errors of performance (SEP). For all three calibration functions, the SEP was less than 1% moisture content. Also, the frequency behavior of each of these calibration functions was examined in the frequency range between 8 and 14 GHz. Among the three density-independent calibration functions, calibration function ψ3 showed the least variation with frequency.

Determination of oil and water in olive and olive pomace by NIR and multivariate analysis

Abstract: Fourier transform (FT) Near Infrared Spectroscopy (NIR) spectrometry in combination with partial least squares (PLS) regression was used for direct, reagent-free determination fat and moisture content in milled olive and olive pomace. The two calibration models obtained were built with samples from two years harvest (2006/2007) and have a good predictive power considering the nature of the samples and are both being used in an industrial plant.

Fingerprint profile of active components for Andrographis paniculata Nees by HPLC-DAD

Abstract: Andrographis paniculata Nees (A. paniculata) has been used as herbal medicine for thousands of years in China. In this work, a quick extraction method with microwave-assisted treatment was studied for a complete extraction of active compounds from A. paniculata. Furthermore, the proposed fingerprint method, enhanced fingerprint by HPLC-DAD, has the advantage of efficiency and accuracy. In comparison with common fingerprint at fixed wavelength, enhanced fingerprint compiled additional spectral data and was hence more informative. It could efficiently identify, distinguish and assess A. paniculata. So it could be used to conduct the quality control of this traditional Chinese medicine comprehensively.

RFID behavior study in enclosed marine container for real time temperature tracking

Abstract: The performance of radio waves in open environments has been studied for years. In contrast, the behavior of Radio Frequency Identification (RFID) inside metal enclosed areas is not yet understood. This research project focuses on the 3-D mapping of RFID signal strength inside a 12 m refrigerated marine container instrumented with three different types of radio frequency (RF) emitters: 915 MHz reader; 2.45 GHz reader and 433 MHz RF transmitter. The main goal is to find a frequency/configuration that would allow real time reading of the temperature in a shipment of perishable products using RFID. Only one frequency and one antenna were used at a time. The RF transmitter antenna was mounted at two different places inside the container; at the top of the front wall (facing back) and on the ceiling in the middle of the container (facing down). The signal strength was acquired by a spectrum analyzer and its antenna was mounted on a small electric cart inside the container. The cart was programmed to move along the length of the container and stop repeatedly, allowing three automated measures per position. All data were analyzed in terms of power level and attenuation. The maps showed that the RFID antenna positioned at the front of the container delivered slightly better results than the one in the middle of the ceiling. The results showed a significantly higher performance at the 433 MHz level.

Onion sour skin detection using a gas sensor array and support vector machine

Abstract: Onion is a major vegetable crop in the world. However, various plant diseases, including sour skin caused by Burkholderia cepacia, pose a great threat to the onion industry by reducing shelf-life and are responsible for significant postharvest losses in both conventional and controlled atmosphere (CA) storage. This study investigated a new sensing approach to detect sour skin using a gas sensor array and the support vector machine (SVM). Sour skin infected onions were put in a concentration chamber for headspace accumulation and measured three to six days after inoculation. Principal component analysis (PCA) score plots showed two distinct clusters formed by healthy and sour skin infected onions. The MANOVA statistical test further proved the hypothesis that the responses of the gas sensor array to healthy onion bulbs and sour skin infected onion bulbs are significantly different (P < 0.0001). The support vector machine was employed for the classification model development. The study was undertaken in two phases: model training and cross-validation within the training datasets and model validation using new datasets. The performances of three feature selection schemes were compared using the trained SVM model. The classification results showed that although the six-sensor scheme (with 81% sensor reduction) had a slightly lower correct classification rate in the training phase, it significantly outperformed its counterparts in the validation phase (85% vs. 69% and 67%). This result proved that effective feature selection strategy could improve the discrimination power of the gas sensor array. This study demonstrated the feasibility of using a gas sensor array coupled with the SVM for the detection of sour skin in sweet onion bulbs. Early detection of sour skin will help reduce postharvest losses and secondary spread of bacteria in storage.

Estimation of olive oil acidity using FT-IR and partial least squares regression

Abstract: Olive oil characteristics are directly related to olive quality Information about olive quality is of paramount importance to olive and olive oil producers, in order to establish its price Real-time characterization of the olives avoids mixtures of high quality with low quality fruits, and allows improvement of olive oil quality This work describes an indirect determination of olive acidity and that allows a rapid evaluation of olive oil quality The applied method combines chemical analysis (30 min Soxhlet olive pomace extraction) in tandem with a spectroscopic technique (FT-IR) and multivariate regression (PLS1) The most suitable calibration model found used SNV pre-processing and was built with 4 Latent Variables giving a RMSECV of 87% and a Q2 of 097 This accurate calibration model allows the estimation of olive acidity using a FT-IR spectrum of the corresponding Soxhlet oil dry extract and therefore is a suitable method for indirect determination of FFA in olives

Characterization of commercial chili sauce varieties according to their chemical and physical properties using chemometric methods

Abstract: Different varieties of chili sauces that are commercially available in Malaysia were randomly collected and their physical and chemical parameters were determined. Initially, pattern recognition techniques were applied in order to categorize the chili sauce varieties. With the aid of principal component analysis and cluster analysis, it is possible to visualize the clustering tendencies of the different varieties of chili sauces where four major clusters, namely chili sauces, general chili ketchups, hot/garlic added ketchups, and Thai ketchups have been successfully partitioned. Color and taste parameters have been found to be the most discriminating variables among them. With the K-nearest neighbor technique, a good prediction of chili sauces’ categories could be achieved. With appropriate customer preference survey, it would be possible to map these characteristics to the preferences. This would certainly help producers of chili sauces in upgrading the quality and taste of their products.

Studying physiochemical changes in a new pasta product containing beef heart by vibrational spectroscopy

Abstract: A new pasta product was developed by partially replacing durum wheat flour with beef heart to enhance its nutritional value. Physiochemical changes of the pasta were investigated by vibrational spectroscopy, namely Fourier transform infrared (FT-IR) and Raman spectroscopy. Relationships between pasta texture and the intensity of vibrational spectra were established. Lipid-protein complex formation, β-sheet arrangement, degree of polysaccharide polymerization, and cysteine thiol group were related to hardness and chewiness of pasta. The lipid portion and β-sheet structure were two significant parameters for explaining pasta adhesiveness, while pasta firmness might be related to β-sheet alignment and the polysaccharide network. Pasta cohesiveness might involve the α-helical structures and hydrogen bonding formation in the gluten network. However, no variable met the p < 0.1 significance level for inclusion into the model to explain pasta springiness. These results reveal that FT-IR and Raman spectroscopy could be employed to evaluate the physical chemistry of pasta and showed a potential use for quality assessment in pasta products.

Improved discrimination of soft and hard white wheat using SKCS and imaging parameters

Abstract: Natural variation of hardness of wheat kernels often results in overlapping hardness indices (HI) distributions between hard and soft classes as measured with the single kernel characterization system (SKCS). This is particularly true for the case of the hard white (HW) and soft white (SW) wheat classes. To address this problem, a color camera was incorporated into the SKCS system so that color and kernel size data could be combined with SKCS measurements for classification purposes. Samples of hard red (HR), soft red (SR), HW, and SW wheat were classified using the SKCS system with and without the camera and results compared. Using the camera system, errors for separating HW from SW classes were reduced to less than 5%, as compared to 17.1% using SKCS alone. Furthermore, improved data processing applied to the low-level data currently produced by the SKCS system led to greater than 50% reduction in classification errors between SW and HR as compared to using HI data alone. Similar improvements in classification accuracies for 300-kernel sample containing mixtures of SW and HW were also achieved. The 300 kernel sample classification is usually what inspectors and grain traders use to determine sample purity rather than individual kernel results. The techniques developed should aid grain inspectors in properly identifying mixtures of these two classes. Unfortunately, for the SR and HR classes, incorporating the camera data decreased classification accuracy while increasing the complexity of the system. However, SR and HR classes can be adequately distinguished with the SKCS in its current form.

Evaluation of reactive oxygen species generating AirOcare system for reducing airborne microbial populations in a meat processing plant

Abstract: The microbial contamination of meat and meat products is of continuing concern to the meat industry and regulatory agencies. Air has been established as a source of microbial contamination in slaughter and processing facilities. The objective of this research was to determine the efficacy of reactive oxygen species (ROS) generating AirOcare equipment in reducing airborne bacteria in a meat processing environment. Bacterial strains found in ground beef were used to artificially contaminate the air using a 6-jet Collison nebulizer. Airborne bacterial populations in the meat processing room were monitored every 24 h at multiple locations using a Staplex 6 stage air sampler. Total aerobic, Gram-negative, and lactic acid bacterial populations were determined by sampling on R2A agar, MacConkey agar and Lactobacilli MRS agar, respectively. Approximately 3 log reductions of lactic acid bacteria and Gram-negative bacteria were observed after 24 h of treatment (p < 0.05) compared to ~1.5 log reduction in the control treatment. Further exposure with ROS significantly reduced lactic acid bacteria and Gram-negative bacteria in the air at 48 and 96 h sampling intervals. These findings reveal that reactive oxygen species treatment using AirOcare unit significantly reduces airborne contamination in a meat processing environment.

RF/microwave technology application for identification of stored-grain insects under electric fields

Abstract: The application of microwave technology to identify the species and quantities of seven stored-grain insects was investigated with a new measuring method using a sensor and an electric field device in the frequency range of 0.3–1200 MHz. Three different constant voltages (0, 20, and 40 V) and three different frequencies of alternating electric fields (0.01 Hz, 0.5 MHz, and 5 MHz) were tested to address the possibility of improving the identification of insects. Two-band pairs were optimized in the frequency range to maximize the identification and detection recognition rate using neural network techniques. The new measuring method resulted in high recognition rates for identifying both species and quantity of insects. Recognition rates of 95.5, 83.3, and 90.9% were achieved at the band-pair (660.1 and 768.1 MHz) under 0, 20, and 40 V constant electric fields respectively. Similarly, recognition rates of 90.0, 87.1, and 87.1% were respectively achieved at the band-pair (174.3 and 432.2 MHz) under 0.01 Hz, 0.5 MHz, and 5 MHz alternating electric fields.

Food process automation

Abstract: For the conversion of agricultural commodities into consumable and marketable food products, science and technology are now enabling amazing advances in food process automation. Mechanization has achieved startling reductions in the labor requirements of processing operations between farm and fork, and now automation has begun reducing the considerable human sensory input component involved in many food processing operations. Automation allows processors to satisfy high-volume demand and distribution needs with greater efficiency while also providing greater flexibility and responsiveness in maintaining and improving food quality and safety. Food safety remains bigger than ever in consumer awareness, with incidents of contamination and outbreaks of food borne illness rooted in problems ranging across the food system, from the fields and livestock of producers to the operations of processors, distributors, and retail establishments. Solutions to many of these problems can be effectively implemented through automation, provided that new sensing technologies are appropriately developed for specialized applications in the food production and distribution system, and that both technical knowledge and understanding of food safety and security needs are shared between those who develop the automation technologies and those who will implement them. Food process automation will depend on both fundamental and applied research in sensor technologies for process control and monitoring, as well as data management strategies for product tracking and traceability, packaging and distribution, and sanitation and inspection. This special issue of Sensing and Instrumentation for Food Quality and Safety highlights some of the new technologies, ideas and research findings currently being developed for food process automation by researchers from academia, government, and industry worldwide. The articles in this special issue cover recent advances in different aspects of food process automation, and also provide overviews of spectroscopy-based monitoring technologies and of systems, standards, and interfaces for automated food processing operations. Ghoush and Jayas first present the basic working principles of a broad spectral range of techniques currently used for automated food process monitoring, ranging from ultraviolet spectroscopy to nuclear magnetic resonance. Significant quality and safety improvements in food processing have come about from the integration of these nondestructive techniques. Next is a broad review by Mahalik of systems, standards, and interfaces for advanced automation in the food process industry, covering a variety of technologies for processing and packaging and the current state of the art. The use of radio frequency identification (RFID) is a significant and growing trend in food distribution system management. Amador et al. present the use of RFID technologies for process traceability, with particular focus on real-time monitoring the environment of food products along the supply chain to enable rapid decision making and the capture of long duration temperature profiles. Laniel K. Chao (&) M. S. Kim Environmental Microbial and Food Safety Lab, USDA, ARS, Beltsville, MD, USA e-mail: kevin.chao@ars.usda.gov

Iron determination by FAAS in fish feed and feces after ultrasound-assisted extraction

Abstract: This paper proposes a method to determine iron in samples of fish feed and feces using ultrasound in the extraction of the analyte and in subsequent quantification by flame atomic absorption spectrometry. Using HCl 0.10 mol L−1 as the extraction solution, the optimal conditions of extraction were found to be: granulometry of the sample <60 μm; a sonication time of five cycles of 10 s and sonication power of 136 W. The method was applied in studies of the availability of iron in four food sources used in the diet of Nile Tilapia. The results obtained with the proposed extraction method allowed us to calculate the coefficients of apparent digestibility of iron in the food sources, which was not possible when using results obtained from samples mineralized by acid digestion.

Multiparametric probe as screening test for quality and safety evaluation in foods

Abstract: A probe for a screening evaluation test of quality and safety of foods based on four different amperometric biosensors all having Clark’s electrode as transducer and different biological systems as components is described. Specifically three amperometric biosensors (tyrosinase, cyclooxygenase and superoxide dismutase biosensor) and a respirometric test are the components of the probe. For each proposed sensor robustness and LODs were evaluated. The latter ones for each analyte are compared with Maximum Residue Limits (MRLs) admitted in foods according to EEC Norm. Lastly, two different simple extraction procedures of the analytes to be detected from the food matrix have been planned and set up, in any case obtaining more than 70% extracted.