Technologies for Enhancing Pecan Production and Processing (original) (raw)
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Indian J.Sci.Res., 2014
This paper presents an intelligent based system for grading hulled and non-hulled pistachio nuts. The first step in the pistachio nuts preparation process is to hull and to peel the picked crops. In pistachio preparation terminals, the manual separation of hulled pistachios from those without hull, increases price and contamination and decreases the quality. The proposed research introduces an automatic system based on image processing technique to enhance the accuracy in detecting the hulled and non-hulled pistachio nuts. Three ordinary classifiers including: Multilayer Feed forward Neural Network (MFNN), Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) are then applied to select the best classifier for pistachio nuts process. To evaluate the performance of the system 200 pistachio nuts from four native Iranian pistachio nuts varieties are used. Each variety consists of 100 hulled and 100 non-hulled nuts. Experimental results show an accuracy of 95% and average time period of about 0.02 second for the best selection of the system. The pistachio tree belongs to the family anacardiaceae, of which the cashew, mango, sumac and poison oak are also members. There are more than sixty species of pistachio in different regions of Iran. One of the important steps of pistachio nuts grading is to separate the non-hulled pistachio nuts from the hulled ones. Pistachios processing factories begin to hull pistachio nuts after harvesting process. In practice, non-hulled pistachio nuts might be separated manually from the hulled ones at the end of the processing line and with traditional methods. Insects, fungus and aflatoxin increasing are also observed due to the unpeeled crops. Consequently, the price and the quantity of the harvested crops would be reduced [1, 2]. At the end of the line processing, samples would be classified into five groups in order to their type of hull as shown in fig. 1. Figure 1: Types of pistachios at the end of processing line: (A) the image of cracked hulled pistachio(open), (B) non-hulled pistachio, (C) half-hulled pistachio, (D) non-cracked hulled pistachio(close), (E) cracked, hulled pistachio as wastes. In this research, a mechatronics system is proposed to select samples one by one and to separates hulled and non-hulled samples automatically by processing the images. In half-hulled type, at least %15 of the pistachio nuts is hulled. Some methods and solutions are proposed to recognize cracked and semi-cracked pistachios [3]. In order to decrease Aflatoxin (A toxin specifically to plant) and increase the quality of the exported pistachio, some works are conducted as follows: Pearson et al. proposed a sorting system which has been developed for the separation of small in-shell pistachio nuts from kernels without shells on the basis of vibration generated when moving samples strike a steel plate. Impacts between the steel plate and the hard shells are measured using an accelerometer attached to the bottom of the plate, produce higher frequency signals than impacts between the plate and the kernels. On the other hand, signal amplitudes were highly variable which made them useless for the separation of samples. They developed another system by using both amplitude and frequency information to classify the signals. The algorithm activated an air nozzle to divert in-shell nuts away from the kernel stream. A prototype sorter was tested at throughput rates of 0.33, 10, 20, and 40 nuts per second using a mix of 10% in-shell and 90% kernels at the lowest throughput rate; classification accuracies were 96% for in-shell nuts and 99% for kernels. For throughput rates between 10 and 40 nuts/s, correct classification ranged from 84 to 90% for in-shell nuts. For kernels, accuracy was 95% at 10 and 20 nuts/s and 89% at 40 nuts/s [4]. Another research is led in California to grade pistachios with shell by image processing; this research illustrates the feasibility of using color imaging
Quality indices and sensory scores support early harvest of pecans
HortTechnology, 1995
Summary. Pecans [ Carya illinoinensis (Wangenh. C.) Koch] were harvested weekly for 9 and 7 weeks until normal harvest time during 1986 and 1987, respectively. Kernels were tested for chemical, physical, and sensory properties. Moisture decreased from 13% at initial harvest time to 4% to 6% by normal harvest. Free fatty acids decreased from 1 Food process engineer and associate professor,
Pecan nut and kernel traits are related to shelling efficiency
HortScience, 2003
The U.S. Dept. of Agriculture, Agricultural Research Service conducts the largest and oldest pecan [Carya illinoinensis (Wangenh.) K. Koch] breeding program in the world. This program evaluates thousands of nut and kernel samples each year using a standard nut and kernel evaluation system developed and refined for more than 70 years. This report relates the effectiveness of these evaluations to commercial shelling efficiency by direct comparison of these data to commercially shelled samples from the same clone performance test. Visual ratings of shelled kernel samples (1-5, with 1 = best) were correlated with time required to hand clean kernel samples (r = 0.55). As percent kernel increased, visual ratings of shelled kernels improved (decreased) (r =-0.73). More intact halves were recovered from shelled samples with the best (lowest) visual ratings (r =-0.67). Conversely, fewer pieces of any size were present in samples with the best visual ratings. Visual ratings improved as nut density decreased (r = 0.33). Samples with the lightest kernel color also had the best visual ratings (r = 0.38). These data show that the standard U.S. Dept. of Agriculture pecan nut and kernel evaluation system needs to be refined by modifying selection pressure placed on various standard evaluation traits.
Technological development for the optimization of the extraction of pecan nuts
2020
The objective of this article is to show the results derived from a proposal for the optimization of the extraction process of the Carya illinoinensis Koch walnut, implementing the use of technological development as a fundamental part of the process. The foregoing, in search of new technological tools for the producers of this fruit and thus contribute to economic development by reducing costs, increasing efficiency and increasing production by optimizing processes. This investigation takes as a sample a local producer located in the rural community ‘Lo de Nava’, Jerez de García Salinas, Zacatecas. To carry out the extraction process, 100 walnut samples were analyzed, of which 45 had the extraction process applied manually and the remaining 55 had an optimized process for the separation of the almond, this with in order to collect the data for statistical analysis and know the results of the research approach. That said, the proposal to address the problems outlined is the developm...
Low Cost Real-Time Sorting of In-Shell Pistachio Nuts from Kernels
Applied Engineering in Agriculture, 2008
A simple, low-cost optical system and decision making circuitry for use in high speed sorting devices designed for separating pistachio nuts with (in-shell) and without (kernels) shells is reported. Testing indicates 95% accuracy in removing kernels from the in-shell stream with no false positive results out of 1000 kernels tested. Testing with 1000 each of in-shell, shell halves, and kernels resulted in an overall error of about 3.3%, roughly twice the overall error rate achieved using a commercially available dual band NIR-VIS sorting device. However, the cost of materials for the equipment reported here was less than $500 (U.S.), indicating the potential for economical sorting versus for commercially available equipment. Since existing sorters can be trained to sort a variety of product streams, implementation of the new device in pistachio plants could free up machines for other sorting tasks, thus reducing the overall cost of sorting the pistachio crop.
Non-destructive quality determination of pecans using soft X-rays
Postharvest Biology and Technology - POSTHARVEST BIOL TECHNOL, 2007
Use of soft X-ray digital imaging for non-destructive quality evaluation of pecans was explored. Unshelled pecans were imaged at various X-ray tube voltages from 15 to 50kVp and currents from 0.1 to 1mA. Pecan images with good contrast image were identified. The cavity inside the pecan shell and the nutmeat portion were segmented manually in the pecan radiographs. Percent nutmeat area, mean pixel intensity, and local intensity variation adequately determined nutmeat quality, non-destructively. Pecan nutmeat weight was estimated with an error of less than 10% from images taken at 35kVp–0.75mA, 40kVp–0.5mA, and 45kVp–0.5mA. Defects and insects were clearly differentiated in X-ray images after applying contrast stretching or high-frequency emphasis techniques.
Postharvest Biology and Technology, 2017
The storage quality of shelled peanuts during storage were assessed using hybrid electronic nose (e-nose)-fuzzy logic approach, beyond conventional tests. Fuzzy logic was used to rank and screen best responsive MOS sensors (total 18) to detect global rancid odors from aged peanuts. Using e-nose data, an odor index (OI) was estimated and correlated with chemical rancidity indices (peroxide value (PV) and acid value (AV)). Multiple linear regressions (MLR) were used to predict the storage time and rancidity indices of peanuts using response data of fuzzified sensors. Fuzzy interpretation identified four sensors which best classified aged and deliberately rancid peanuts using principal component and hierarchical cluster analysis. E-nose data closely predicted the storage time of peanuts relative to chemical rancidity indices (R 2 , 0.993; RMSE, 3.31 vs. R 2 , 0.985; RMSE, 4.57) (p > 0.05). In addition, it predicted the rancidity indices with accuracy (PV: R 2 = 0.995, RMSE = 0.29; AV: R 2 = 0.989, RMSE = 0.19). OI of peanuts was highly correlated with PV (0.99) and AV (0.96) and estimated their discard time (basis threshold PV = O 2 at 10 mmol kg −1) as 99 d (e-nose) vs. 97 d (conventional tests). The presented approach could be adopted as non-destructive alternative to conventional tests to assure post-harvest quality of shelled peanuts at agro-industrial settings.
Pecan Propagation: Seed Mass as a Reliable Tool for Seed Selection
Horticulturae, 2018
Pecan is one of the most important horticultural nut crops in the world. It is a deciduous species native to the temperate zones of North America, introduced into the subtropical regions of Brazil during the 1870s. High quality seedlings are essential to establishing healthy and productive orchards, and selection of seeds is an important factor in this issue. In this study we evaluated the correlation between seed mass, emergence rate and morphometric traits of seedlings in the pecan cultivar Importada. A significant positive correlation (r > 0.81) between seed mass and plantlet height, stem diameter, emergence rate and number of leaves was observed. Our results suggest that seed mass can be used as a direct method for seed selection towards production of vigorous pecan seedlings. However, since an increase in seed mass is usually associated with a decrease in the number of seeds that a plant can produce per unit canopy, long-duration studies are recommended in order to evaluate ...
Applied Sciences
The existence of conversion industries to sort and grade hazelnuts with modern technology plays a vital role in export. Since most of the hazelnuts produced in Iran are exported to domestic and foreign markets without sorting and grading, it is necessary to have a well-functioning smart system to create added value, reduce waste, increase shelf life, and provide a better product delivery. In this study, a method is introduced to sort and grade hazelnuts by integrating audio signal processing and artificial neural network techniques. A system was designed and developed in which the produced sound, due to the collision of the hazelnut with a steel disk, was taken by the microphone placed under the steel disk and transferred to a PC via a sound card. Then, it was stored and processed by a program written in MATLAB software. A piezoelectric sensor and a circuit were used to eliminate additional ambient noise. The time-domain and wavelet domain features of the data were extracted using M...