A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage

The quality of potato tubers is dependent on several attributes been maintained at appropriate levels during storage. One of these attributes is sprouting activity that is initiated from meristematic regions of the tubers (eyes). Sprouting activity is a major problem that contributes to reduced shelf life and elevated sugar content, which affects the marketability of seed tubers as well as fried products. This study compared the capabilities of three different optical systems (1: visible/near-infrared (Vis/NIR) interactance spectroscopy, 2: Vis/NIR hyperspectral imaging, 3: NIR transmittance) and machine learning methods to detect sprouting activity in potatoes based on the primordial leaf count (LC). The study was conducted on Frito Lay 1879 and Russet Norkotah cultivars stored at different temperatures and classification models were developed that considered both cultivars combined and classified the tubers as having either high or low sprouting activity. Measurements were performed on whole tubers and sliced samples to see the effect this would have on identifying sprouting activity. Sequential forward selection was applied for wavelength selection and the classification was carried out using K-nearest neighbor, partial least squares discriminant analysis, and soft independent modeling class analogy. The highest classification accuracy values obtained by the hyperspectral imaging system and was 87.5% and 90% for sliced and whole samples, respectively. Data fusion did not show classification improvement for whole tubers, whereas a 7.5% classification accuracy increase was illustrated for sliced samples. By investigating different optical techniques and machine learning methods, this study provides a first step toward developing a handheld optical device for early detection of sprouting activity, enabling advanced aid potato storage management

Changes in management function of control

Controlling is a constantly ongoing managerial process of designing standards, measuring performance, comparing the performance with standards, and implementing corrective actions to ensure effective and efficient running of the organization's activities. Controlling represents one of the basic functions in management in Anglo-American understanding. The original term has been changed from control to controlling, as control is (like a plan in planning) only a small part of long-term activity. The term controlling, however, is also used in German literature, where it represents what Anglo-American literature refers as management (or managerial) accounting. As the Central and Eastern European literature is heavily influenced by German literature, in English-written papers published in Europe confusions often happen. Based on results of our questionnaire survey in 331 companies operating in Slovakia, which collected data at the turn of 2016 and 2017, we analyze the changes in management function of controlling and compare them with the findings in literature. We analyze the research results according to the different characteristics of the research sample, such as the size of the company by number of employees, the economic result, the respondent's position in the organizational structure of the company, or the respondent's attitude if he/she is an object or subject of control. Taking into account the quantitative and qualitative results obtained, we also present specific changes in the control of our businesses

Optimization and Validation of Rancimat Operational Parameters to Determine Walnut Oil Oxidative Stability

This study was performed to optimize and validate Rancimat (Metrohm Ltd., Herisau, Switzerland) operational parameters including temperature, air-flow, and sample weight to minimize Induction-Time (IT) and IT-Coefficient-of-Variation (CV), using Response Surface Methodology (RSM). According to a Box–Behnken experimental design, walnut oil equivalent to 3-, 6-, or 9-g was added to each reaction vessel and heated to 100, 110, or 120 °C, while an air-flow equal to 10-, 15-, or 20-L·h−1 was forced through the reaction vessels. A stationary point was found per response variable (IT and CV), and optimal parameters were defined considering the determined stationary points for both response variables at 100 °C, 25 L·h−1, and 3.9 g. Optimal parameters provided an IT of 5.42 ± 0.02 h with a CV of 1.25 ± 0.83%. RSM proved to be a useful methodology to find Rancimat operational parameters that translate to accurate and efficient values of walnut oil IT

Design and optimization of an air distributor for an almond stockpile heated and ambient air dryer (SHAD) - Part 1

A stockpile heated and ambient air dryer (SHAD) was developed as an alternative to conventional almond windrow drying. Previous experiments showe that the drying air produced by SHAD was undesirably distributed through the almond stockpile. Therefore, an air distributor was developed containing 12 outlets, arranged in 4 rows of 3 outlets each. This study describes the comprehensive process of the air distributor design, manufacturing, and its optimization. The optimization process employed both computational fluid dynamics simulations and in-field airflow validation measurements. Initial 4-row air distributor in-field validation measurements indicated airflow distribution percentages were 4.1%, 30.8%, 44.9%, and 20.2% for the outlets in rows 1 through 4. This showed that almonds located around row 1 would not receive sufficient air to properly dry. Thus, an optimized 3-row air distributor configuration was developed and validated to yield an airflow distribution percentage of 31.3%, 44.4%, and 24.3% for outlets in the second to fourth rows, respectively. The 3-row air distributor configuration is therefore desirable, as the middle and tallest section of the stockpile will receive the highest airflow. The air distributor therefore markedly enhanced the SHAD's air supply distribution

Prediction of the Leaf Primordia of Potato Tubers Using Sensor Fusion and Wavelength Selection

The sprouting of potato tubers during storage is a significant problem that suppresses obtaining high quality seeds or fried products. In this study, the potential of fusing data obtained from visible (VIS)/near-infrared (NIR) spectroscopic and hyperspectral imaging systems was investigated, to improve the prediction of primordial leaf count as a significant sign for tubers sprouting. Electronic and lab measurements were conducted on whole tubers of Frito Lay 1879 (FL1879) and Russet Norkotah (R.Norkotah) potato cultivars. The interval partial least squares (IPLS) technique was adopted to extract the most effective wavelengths for both systems. Linear regression was utilized using partial least squares regression (PLSR), and the best calibration model was chosen using four-fold cross-validation. Then the prediction models were obtained using separate test data sets. Prediction results were enhanced compared with those obtained from individual systems’ models. The values of the correlation coefficient (the ratio between performance to deviation, or r(RPD)) were 0.95(3.01) and 0.9s6(3.55) for FL1879 and R.Norkotah, respectively, which represented a feasible improvement by 6.7%(35.6%) and 24.7%(136.7%) for FL1879 and R.Norkotah, respectively. The proposed study shows the possibility of building a rapid, noninvasive, and accurate system or device that requires minimal or no sample preparation to track the sprouting activity of stored potato tubers

Drying of freshly harvested almonds using a stockpile heated and ambient air dryer (SHAD) with an air distributor -part 2

An almond stockpile heated and ambient air dryer (SHAD) without an air distributor, did not adequately distribute air throughout the stockpile. Therefore, this project evaluated the effect of adding an air distributor within the SHAD A-frame as an alternative method to conventional windrow drying. Three stockpile drying tests were performed using ‘Nonpareil’, ‘Winter’, and ‘Monterey’ almond varieties with different initial (fresh) weights and kernel dry-basis moisture contents (MC) equal to 4763 kg and 11.8%, 2585 kg, and 11.5%, and 6849 kg and 21.5%, respectively. All tests were directly compared to conventional windrow drying. Almond quality parameters, including kernel MC, color, lipid oxidative stability, peroxide value, free fatty acid content, internal cavities, and insect injury were measured before and after drying. The SHAD with the air distributor properly maintained almond quality, while uniformly dehydrating almonds to the desired MC of ≤6 % within 7 days. Conventional windrow drying took up to 13.6 days, and the desired final MC was only achieved with the ‘Monterey’ variety. Thus, the SHAD fitted with a well-designed air distributor can be used to dehydrate almonds in a stockpile as an alternative to conventional windrow drying

Development of a Stockpile Heated and Ambient Air Dryer (SHAD) for Freshly Harvested Almonds

Dust generated by farming activities is a safety hazard to farmworkers and an environmental contaminant. During the almond (Prunus dulcis) harvest in California, dust is primarily generated by the mechanized movement of almonds disturbing the bare soil of the orchard floor, during the sun-drying, windrowing process, and as they are transferred into trucks for transport to processing facilities. Off-ground dust-less harvesting will only be achieved when the almond industry adopts feasible mechanical drying methods. Therefore, a stockpile heated and ambient air dryer (SHAD) was developed to determine the feasibility of dehydrating almonds (Var. ‘Monterey’). A stockpile containing 4,155 kg of almonds was created and almonds were dehydrated from their initial 12.6% almond kernel dry-basis moisture content (MCdb) to final MCdb of 6.04%. Drying was achieved as a combination of heated air at a temperature of 55°C in the drying plenum with airflow of 0.078 m3/s per m3 of fresh almonds. After drying, almond quality parameters were measured, including damage by molds or decay, insect injury, and presence of internal cavities. Drying energy consumption, cost, and performance indicators were also determined. The differences in MCdb between the bottom, middle, and top layers of the almond stockpile were significant (p ≤ 0.05). Post-hoc Tuckey test was conducted which indicated that the MCdb in the top layer was significantly lower than almond MCdb in the middle and bottom layers. Results showed that damage by molds or decay, insect injury, and internal cavities were 1.81%, 0%, and 1.77%, respectively, after drying. Therefore, the overall almond quality was not compromised. The drying process cost $11.65 per tonne of the initial weight of almonds with a Specific Moisture Extraction Rate (SMER) of 0.64 kg/kWh, Moisture Extraction Rate (MER) of 1.02 kg/h, and a Coefficient of Performance (COP) of 1.33. Comparison with other dryers in the literature shows that SMER and MER were within limits. However, a low COP was observed

The use of desiccants for proper moisture preservation in green coffee during storage and transportation

Prolonged storage and long-distance transportation of green coffee beans exposes them to undesirable fluctuations in temperature (T) and relative humidity (r.h.), which can change the physical (wet-basis moisture content (MCwb), water activity (Aw), and color) and sensory characteristics of the coffee. High humidity also supports mold growth, decay, and microbial activities. Thus, the objective of this study was to evaluate the efficacy of commercially available desiccants for preserving the moisture content of green coffee between 10 and 12% MCwb, when stored in either hermetic packages and/or jute sacks, and to assess the corresponding impact on sensory quality. A conventional coffee storage and transportation period from Brazil to Italy with a duration of 42 days was mimicked in environmental chambers. Treatments in a 3 × 3 factorial design consisting of three packaging materials (GrainPro SuperGrain bag, GrainPro TranSafeliner, and/or jute sacks) and desiccants (Drying Beads®, CaCl2, or no desiccants) were evaluated. Additionally, four different mass ratios of green coffee to desiccant ranging from 50 to 300 – g coffee per g desiccant were also evaluated. The MCwb, Aw, and color of all samples were measured approximately weekly over 42 days. In comparison to the control (no desiccant, and only jute sacks), we observed a statistically significant impact for all tested desiccants and hermetic packages for maintaining the proper MCwb, Aw, and color. No significant difference was observed for the different desiccant masses tested when they were placed inside the hermetic packaging, but the desiccants were ineffective without the hermetic packaging. Triangle test and descriptive sensory evaluation yielded no significant differences between the use of hermetic packages with or without desiccants

Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer dat