Silvia Arazuri

A Review of the Application of Near-Infrared Spectroscopy for the Analysis of Potatoes

Potato (Solanum tuberosum L.) is one of the most important crops in the world being considered as a staple food in many developing countries. The potato industry like other vegetable and fruit industries is subject to the current demand of quality products. In order to meet this challenge, the food industry is relying on the adoption of nondestructive and environmentally friendly techniques to determine quality of products. Near-infrared spectroscopy (NIRS) is currently one of the most advanced nondestructive technologies regarding instrumentation and application, and it also complies with the environment requirements as it does not generate emissions or waste. This paper reviews research progress on the analysis of potatoes by NIRS both in terms of determination of constituents and classification according to the different constituents of the tubers. A brief description of the fundamentals of NIRS technology and its advantages over other quality assessment techniques is included. Finally, future prospects of the development of NIRS technology at the industrial level are explored.

Apple mealiness detection by non-destructive mechanical impact

Samples of Golden Delicious, Granny Smith, Starking and Top Red apples were stored at 4 °C to develop mealiness. After different storage times, sensory analysis and impact testing from 2 cm high were performed. Except for Granny Smith, all cultivars developed mealiness. A classification of the fruits (unsuitable or not) according with the sensory mealiness degree was performed. Parallel classification was done by discriminate analysis using non-destructive impact test variables. A correlation was established between mealiness and other factors such as storage time and cultivar. Mealiness was also related to the impact test variables, and particularly to the maximum resistance to impact. By processing data using signal detection tools, a classification was achieved prioritising the textural quality of the selected sample. Samples with a total absence of mealiness were obtained from the Golden and Top Red cultivars, but not from Starking Delicious apples.

Phytochemicals determination and classification in purple and red fleshed potato tubers by analytical methods and near infrared spectroscopy

BACKGROUND Over the last two decades, the attractive colours and shapes of pigmented tubers and the increasing concern about the relationship between nutrition and health have contributed to the expansion of their consumption and a specialty market. Thus, we have quantified the concentration of health promoting compounds such as soluble phenolics, monomeric anthocyanins, carotenoids, vitamin C, and hydrophilic antioxidant capacity, in a collection of 18 purple- and red-fleshed potato accessions. RESULTS Cultivars and breeding lines high in vitamin C, such as Blue Congo, Morada and Kasta, have been identified. Deep purple cultivars Violet Queen, Purple Peruvian and Vitelotte showed high levels of soluble phenolics, monomeric anthocyanins, and hydrophilic antioxidant capacity, whereas relatively high carotenoid concentrations were found in partially yellow coloured tubers, such as Morada, Highland Burgundy Red, and Violet Queen. CONCLUSION The present characterisation of cultivars and breeding lines with high concentrations of phytochemicals is an important step both to support the consideration of specialty potatoes as a source of healthy compounds, and to obtain new cultivars with positive nutritional characteristics. Moreover, by using near infrared spectroscopy a non-destructive identification and classification of samples with different levels of phytochemicals is achieved, offering an unquestionable contribution to the potato industry for future automatic discrimination of varieties.

Evaluation of mechanical tomato harvesting using wireless sensors.

The harvesting of processing tomatoes is fully mechanised and it is well known that during harvest, fruits are subjected to mechanical stress causing physical injuries, including skin punctures, pulp and cell rupture. Some wireless sensors have been used for research during recent years with the main purpose of reducing the quality loss of tomato fruits by diminishing the number and intensity of impacts. In this study the IRD (impact recorder device) sensor was used to evaluate several tomato harvesters. The specific objectives were to evaluate the impacts during mechanical harvest using a wireless sensor, to determine the critical points at which damage occurs, and to assess the damage levels. Samples were taken to determine the influence of mechanical harvest on texture, or on other quality characteristics including percentage of damages. From the obtained data it has been possible to identify the critical points where the damages were produced for each one of the five harvester models examined. The highest risk of damage was in zone 1 of the combine—from the cutting system to the colour selector—because the impacts were of higher intensity and hit less absorbing surfaces than in zone 2—from colour selector to discharge. The shaker and exit from the shaker are two of the harvester elements that registered the highest intensity impacts. By adjusting, in a specific way each harvester model, using the results from this research, it has been possible to reduce the tomato damage percentage from 20 to 29% to less than 10%.

Fuzzy Edge Detection on Hyperspectral Images Using Upper and Lower Operators

There exists a continuous research effort aiming to port methods for grayscale image processing to more complex imagery data. Color images were an initial target for such effort, but new technologies have lead to many other types of images. In this work we focus on hyperspectral images. Specifically, we analyze how to adapt the Upper-Lower Edge Detector (ULED) to hyperspectral images; our proposal consist of fusioning band-wise information using OWA operators.

Analysis of the influence of the skin on the near infrared absorbance spectra of potato tubers

NIR spectral data were collected using a Luminar 5030 Miniature “Hand held” AOTFNIR (Acousto-Optic Tunable Filter-Near Infrared) Analyser (Brimrose Corporation of America, Baltimore, Maryland) in reflectance mode. A spectral range of 1100–2300 nm with 601 points (2 nm step) was used to obtain the spectra at room temperature. Each sample was scanned at four different points and the average spectrum was used for subsequent analysis. Each spectrum was an average of 50 scans. Data were then transformed to absorbance for further analysis. Analyses First, an analysis of the two varieties was performed in order to identify undesirable scatter effects. With this purpose in mind, absorbance data of both measurements (peeled and unpeeled) of each potato variety were examined to identify imperfections and to compare the degree of scatter between varieties. Then, the mean absorbance spectrum of both peeled and unpeeled tubers of each variety was obtained to identify the main differences in absorption at different wavelengths in the NIR spectrum. Finally, and with the aim of evaluating whether the differences graphically observed corresponded to the formation of statistically different groups, a classification was performed using support vector machines (SVM). Prior to SVM classification, samples were pre-treated using SNV to reduce the physical variability between the samples as a consequence of the scat

Ability of NIRS technology to determine pesticides in liquid samples at maximum residue levels

BACKGROUND Pesticide residues remaining on food represent a potential risk to consumers health. Determination of these pesticide residues involves tedious procedures of analysis with regard to time and laboratory work. Near-infrared spectroscopy (NIRS) is a possible alternative to these methods. The aim of this research was to evaluate the ability of NIRS to classify two pesticides used for controlling apple fruit pests according to their concentration. Different solutions were prepared, based on the dose recommended by the pesticide producers for apple pest treatments. Spectra were acquired on a spectrophotometer from liquid samples belonging to these solutions. RESULTS Calibration models were developed from liquid samples, following the soft independent modelling of class analogy (SIMCA) analysis method. These models classified between 99 and 100% of the validation samples belonging to different pesticide concentration solutions even at the maximum residue limit level of these products in apple fruit. CONCLUSIONS NIRS technology shows a high potential for identifying pesticides in liquid samples, according to their concentration, at the levels required by the legislation.