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Weeds compete with crops for water and nutrients and can adversely affect crop growth and yield, so it is important to research effective weed control methods. This paper provides an overview of the impact of weeds on crop yield and describes the current state of research on weed management in field herbaceous crops. Physical weed control mainly refers to thermal technologies represented by flame weed control and laser weed control, which can efficiently and accurately remove weeds. Mechanical weed control requires a combination of sensor technologies, machine vision technology, and high-precision navigation to improve weed control accuracy. Biological weed control relies heavily on plant extracts and pathogens to create herbicides, but it is costly, and some can be toxic to mammals. Chemical weed control is a common method, resulting in environmental pollution and weed resistance. To reduce the use of chemical herbicides, scholars have proposed integrated weed management strategies, which combine biological control, control of the seed bank, and improve crop competitiveness. Integrated weed management strategies are considered to be the future direction of weed management. In conclusion, physical, mechanical, biological, and chemical weed control methods are commonly used in weed management. Each method has its applicable scenarios, and the implementation of integrated weed management strategies can lead to better weed control, improving crop yield and quality. The main objective of this review is to organize the research progress on weed management methods for herbaceous crops in the field and to provide a reference for the agricultural sector to develop weed control strategies. Specifically, this paper categorizes weed management methods into four groups, discusses and presents the advantages and disadvantages of the aforementioned weed control methods, and discusses future research directions. [ABSTRACT FROM AUTHOR]

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology's prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. [ABSTRACT FROM AUTHOR]

Accurate assessment of crop emergence helps breeders select appropriate crop genotypes, and farmers make timely field management decisions to increase maize yields. Crop emergence is conventionally quantified by manual calculations to quantify the number and size of seedlings, which is laborious, inefficient, and unreliable and fails to visualize the spatial distribution and uniformity of seedlings. Phenotyping technology based on remote sensing allows for high-throughput evaluation of crop emergence at the early growth stage. This study developed a system for the rapid estimation of maize seedling emergence based on a deep learning algorithm. The RGB images acquired from an unmanned aerial vehicle (UAV) were used to develop the optimal model for the recognition of seedling location, spacing, and size, and the prediction performance of the system was evaluated in three stations during 2021–2022. A case study was conducted to show the evaluation of the system for maize seedlings and combined with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The results show that the system has good prediction performance for maize seedling count with an average R2 value of 0.96 and an accuracy of 92%; however, shadows and planting density influence its accuracy. The prediction accuracy reduces significantly when the planting density is above 90,000 plants/ha. The distribution characteristics of seedling emergence and growth were also calculated based on the average value and variation coefficient of seedling spacing, seedling area, and seedling length. The estimation accuracies for the average value of seedling spacing, the coefficient of variation of seedling spacing, the average value of the seedling area, the coefficient of variation of the seedling area, and the average value of the seedling length were 87.52, 87.55, 82.69, 84.51, and 90.32%, respectively. In conclusion, the proposed system can quickly analyze the maize seeding growth and uniformity characteristics of experimental plots and locate plots with poor maize emergence. [ABSTRACT FROM AUTHOR]

Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented. [ABSTRACT FROM AUTHOR]

Weeds in the field affect the normal growth of lettuce crops by competing with them for resources such as water and sunlight. The increasing costs of weed management and limited herbicide choices are threatening the profitability, yield, and quality of lettuce. The application of intelligent weeding robots is an alternative to control intra-row weeds. The prerequisite for automatic weeding is accurate differentiation and rapid localization of different plants. In this study, a squeeze-and-excitation (SE) network combined with You Only Look Once v5 (SE-YOLOv5x) is proposed for weed-crop classification and lettuce localization in the field. Compared with models including classical support vector machines (SVM), YOLOv5x, single-shot multibox detector (SSD), and faster-RCNN, the SE-YOLOv5x exhibited the highest performance in weed and lettuce plant identifications, with precision, recall, mean average precision (mAP), and F1-score values of 97.6%, 95.6%, 97.1%, and 97.3%, respectively. Based on plant morphological characteristics, the SE-YOLOv5x model detected the location of lettuce stem emerging points in the field with an accuracy of 97.14%. This study demonstrates the capability of SE-YOLOv5x for the classification of lettuce and weeds and the localization of lettuce, which provides theoretical and technical support for automated weed control. [ABSTRACT FROM AUTHOR]

Background: Clinical evidence for the rapidity and effectiveness of fentanyl buccal soluble film (FBSF) in reducing pain intensity of breakthrough cancer pain (BTcP) remains inadequate. This study aimed to evaluate the efficacy of FBSF proportional to the around-the‐clock (ATC) opioid regimens in rapidly relieving the intensity of BTcP episodes by determining the percentage of patients requiring further dose titration. Methods: The study procedure included a dose-finding period followed by a 14-day observation period. Pain intensity was recorded with a Numeric Rating Scale (NRS) at onset and 5, 10, 15, and 30 min after FBSF self-administration. Meaningful pain relief was defined as the final NRS score ≤ 3. Satisfaction survey was conducted for each patient after treatment using the Global Satisfaction Scale. Results: A total of 63 BTcP episodes occurred in 30 cancer patients. Only one patient required rescue medication at first BTcP episode and then achieved meaningful pain relief after titrating FBSF by 200 µg. Most BTcP episodes relieved within 10 min. Of 63 BTcP episodes, 30 (47.6%), 46 (73.0%), and 53 (84.1%) relieved within 5, 10, and 15 min after FBSF administration. Only grade 1/2 adverse events were reported, including somnolence, malaise, and dizziness. Of the 63 BTcP episodes, 82.6% were rated as excellent/good satisfaction with FBSF. Conclusion: FBSF can be administrated "on demand" by cancer patients at the onset of BTcP, providing rapid analgesia by achieving meaningful pain relief within 10 min. Trial registration: This study was retrospectively registered 24 December, 2021 at Clinicaltrial.gov (NCT05209906): https://clinicaltrials.gov/study/NCT05209906. [ABSTRACT FROM AUTHOR]

Soybean is grown worldwide for its high protein and oil content. Weeds compete fiercely for resources, which affects soybean yields. Because of the progressive enhancement of weed resistance to herbicides and the quickly increasing cost of manual weeding, mechanical weed control is becoming the preferred method of weed control. Mechanical weed control finds it difficult to remove intra-row weeds due to the lack of rapid and precise weed/soybean detection and location technology. Rhodamine B (Rh-B) is a systemic crop compound that can be absorbed by soybeans which fluoresces under a specific excitation light. The purpose of this study is to combine systemic crop compounds and computer vision technology for the identification and localization of soybeans in the field. The fluorescence distribution properties of systemic crop compounds in soybeans and their effects on plant growth were explored. The fluorescence was mainly concentrated in soybean cotyledons treated with Rh-B. After a comparison of soybean seedlings treated with nine groups of rhodamine B solutions at different concentrations ranging from 0 to 1440 ppm, the soybeans treated with 180 ppm Rh-B for 24 h received the recommended dosage, resulting in significant fluorescence that did not affect crop growth. Increasing the Rh-B solutions reduced crop biomass, while prolonged treatment times reduced seed germination. The fluorescence produced lasted for 20 days, ensuring a stable signal in the early stages of growth. Additionally, a precise inter-row soybean plant location system based on a fluorescence imaging system with a 96.7% identification accuracy, determined on 300 datasets, was proposed. This article further confirms the potential of crop signaling technology to assist machines in achieving crop identification and localization in the field. [ABSTRACT FROM AUTHOR]

Vegetable crop productivity is susceptible to damage from weed competition, with early season weeds a control priority to prevent significant yield loss. There is an urgent need for a reliable robotic sensing system that can work well in a variety of crops to achieve universal weed/crop differentiation, which would facilitate further development in robotic technologies for farming and bring economic benefits to vegetable production. The aim of this study was to develop a novel technique to create a machine-readable crop plant using a systemic crop signalling compound applied to seeds or transplants. The protocols for the crop signalling method and its detection are described. Rhodamine B (Rh–B) was selected as the signalling compound in this study, because it could be used as a fluorescent tracer, had a unique optical appearance in plants, and had the necessary properties to allow systemic behaviour in vegetable seedlings. The Rh–B tracer was applied to snap bean and the systemic behaviour analysed with a fluorescent macroscope. The uptake of Rh–B varied among treatment methods. The Rh–B uptake through the seed coat of snap beans was found mainly in the seedling hypocotyls. The results for root uptake showed that Rh–B could be more readily transported to the whole plant through the root system as compared to the application to seeds. The midvein and secondary veins of bean leaves showed stronger Rh–B fluorescence than other regions of the leaf. Higher concentrations of Rh–B resulted in greater absorption by the plant. Although the crop signalling compound could follow both seed and root pathways for plant uptake, the uptake based on the root pathway had greater capacity than that of the seed pathway. The use of Rh–B provided a systemic crop signalling compound was discussed on further research and field tested to have application to enhance weed/crop differentiation by automated weeders in vegetable crops. The systemic crop signalling system successfully created a machine-readable signal on vegetable crops, and appeared to be non-destructive, cost effective, efficient and accurate for performing automatic plant care tasks. • A systemic crop signaling system was developed for automated plant care. • 579 nm filter and green light were the optimal combination for signal detection. • Fluorescent compound could follow both seed and root pathways for plant uptake. • Higher concentrations of Rhodamine B resulted in greater absorption by the plant. • The uptake based on the root pathway had greater capacity than the seed pathway. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) and mid-infrared (MIR) hyperspectral techniques in tandem with chemometric analyses were employed for developing multispectral real-time systems allowing the food industry to monitor moisture content (MC) in tubers including various potato and sweet potato products during drying. Multivariate models were established by partial least-squares regression (PLSR), support vector machine regression (SVMR), locally weighted partial least square regression (LWPLSR), and back propagation artificial neural network (BPANN) using full spectral ranges of 10372–6105 cm−1 (Spectral Set I), 3996–600 cm−1 (Spectral Set II), and 1700–900 cm−1 (Spectral Set III). The LWPLSR from Spectral Set I and BPANN from Spectral Set II and III, obtained the highest accuracies for tuber MC prediction. Then, both regression coefficient (RC) and successive projection algorithm (SPA) were respectively used for the selection of feature wavelengths in Spectral Set I, II and III. Instead of choosing many groups of characteristic variables for different varieties of potatoes and sweet potatoes, one set of feature variables for all tubers was selected from each spectral region for the convenience of industrial application. Eventually, six sets of feature wavelengths chosen from Spectral Set I, II and III were used to optimize models. The simplified SPA-LWPLSR from Spectral Set II and SPA-BPANN from Spectral Set III acquired good model performances for the tuber MC prediction, with determination coefficients in prediction (R2P) of 0.950 and 0.904, respectively. The RC-BPANN model from Spectral Set I achieved the highest R2P of 0.965. Such accuracies were comparable to that of full spectral models. The results reveal that hyperspectral techniques have great potential in the food industry for real-time measurement of tuber MC. [ABSTRACT FROM AUTHOR]

Investigating the translocation behaviour of fluorescent markers is significant for the effective application of the markers in weed and crop differentiation. Snap bean was used as a model plant to study the systemic movement of Rhodamine B (Rh-B) in specialty crops for weed control. A fluorescence imaging system was developed to monitor the uptake and translocation of Rh-B from dyed snap bean seeds to bean plants. Bean samples were dyed using different concentrations of Rh-B solutions. As the concentration of Rh-B applied to the seeds increased, the fluorescent signal of the marker was at first enhanced, then weakened. After germination, this marker was observed from the stems of bean seedlings at stages of first leaf to multiple leaves over time. The fluorescence response on the hypocotyl was stronger than that on the epicotyl, while there was limited translocation observed in plant roots and leaves based on this fluorescence imaging system. The fluorescence peak at 590 nm, measured by a spectrometer (350–1050 nm), exhibited the greatest contrast between untreated and treated plant samples. The proposed crop signalling approach based on Rh-B emission was able to classify snap bean plants from different weeds (e.g. burning nettle, groundsel, and barley). The results demonstrate that fluorescence imaging technology is a rapid and effective approach to studying the real-time translocation behaviour of a signalling marker in a crop system. Based on the unique fluorescence property, visualisation of the marker in vivo specialty crops grown from Rh-B treated seeds provides potential for their successful application in early season weed discrimination. • Fluorescence imaging was used to characterise a signalling marker in snap beans. • The uptake of Rhodamine from dyed snap bean seeds to bean plants was observed. • The marker was detected from crop stems at first leaf stage to multiple leaves. • Snap bean plants were classified from different weeds based on crop signalling. [ABSTRACT FROM AUTHOR]

Weeds have a serious impact on lettuce cultivation. Weeding is an efficient way to increase lettuce yields. Due to the increasing costs of labor and the harm of herbicides to the environment, there is an increasing need to develop a mechanical weeding robot to remove weeds. Accurate weed recognition and crop localization are prerequisites for automatic weeding in precision agriculture. In this study, an intra-row weeding system is developed based on a vision system and open/close weeding knives. This vision system combines the improved you only look once v5 (YOLOv5) identification model and the lettuce–weed localization method. Compared with models including YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5n, and YOLOv5x, the optimized SPH-YOLOv5x model exhibited the best performance in identifying, with precision, recall, F1-score, and mean average precision (mAP) value of 95%, 93.32%, 94.1% and 96%, respectively. The proposed weed control system successfully removed the intra-row weeds with 80.25% accuracy at 3.28 km/h. This study demonstrates the robustness and efficacy of the automatic system for intra-row weed control in lettuce. [ABSTRACT FROM AUTHOR]

Tuber ultimate compressive strength (UCS) is a very important sensory property of tuber products. The UCS depends significantly on tuber moisture content and cooking degree. Therefore, it is necessary to monitor the UCS of tuber products during the industrial production. In the present study, Fourier transform mid-infrared spectra and UCS reference values of tuber samples at different drying times were collected. The fitting functions between tuber UCS and drying time were evaluated. Moreover, the fingerprint spectral matrices (1500–900 cm−1) were studied by pre-treatment methods and principal component analysis. Then, the spectral features were linked to the tuber UCS based on multivariate calibration models in terms of principal component regression and support vector machine regression (SVMR). It has been confirmed that tuber UCS values fitted by specific exponential function could be successfully used to develop robust SVMR model. [ABSTRACT FROM AUTHOR]

Near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy were explored in the current study to investigate how constituent elements of sweet potato change during cooking, and in the meantime, to identify sweet potato varieties. Partial least square discriminant analysis (PLSDA) model was established to classify varieties of sweet potato, and the correct classification rate of the PLSDA model using Spectral Set I (964–1645 nm) reached as high as 100%. Competitive adaptive reweighted sampling (CARS) was introduced to choose incipient feature wavelengths from three spectral subsets related to tuber cooking loss (CL). Based on 8 feature variables from Spectral Set I, CARS-SVMR model performed best with the highest coefficient of determination in prediction (R 2 P) of 0.893 and the lowest root mean square error of prediction (RMSEP) of 0.075. Then, these three subsets of feature wavelengths selected by CARS were re-optimised by using successive projections algorithm (SPA). With 7 feature variables from Spectral Set II (3996–600 cm−1) suggested by CARS-SPA, the CARS-SPA-PLSR model predicted tuber CL with R 2 P of 0.773 and RMSEP of 0.079. Moreover, the CARS-SPA-PLSR model using 5 wavelengths from Spectral Set I exhibited good prediction result, with R 2 P of 0.913 and RMSEP of 0.058. Although both techniques are capable of determining sweet potato CL in an effective way, the NIR technology demonstrates better predictive capability based on the reduced CARS-SPA-PLSR model. Highlights • IR imaging spectroscopy was proposed to characterise sweet potato samples. • Sweet potato varieties were identified by the PLSDA with NIR and MIR spectra. • PLSR and SVMR were compared using the feature variables selected by CARS-SPA. • CARS-SPA-PLSR model presented acceptable accuracy in the prediction of tuber CL. • NIR hyperspectral imaging showed better performance than MIR microspectroscopy. [ABSTRACT FROM AUTHOR]

Time series spectroscopic and textural analysis data were obtained from 5 varieties of tuber samples during microwave baking. These data were analyzed using evolutionary computing methods including partial least square discriminant analysis (PLSDA), partial least square regression (PLSR) and locally weighted partial least squares regression (LWPLSR). PLSDA was able to discriminate the tuber samples into three separate classes corresponding to their spectral properties. The predictability of spectra in full wavenumber region (4000–600 cm −1 ) and fingerprint region (1500–900 cm −1 ) were calculated using PLSR and LWPLSR and the relative performances of developed models were compared. It was observed that similar or even better predictions were obtained by models using spectra in the fingerprint region. Then, first-derivative and mean centering iteration algorithm (FMCIA) was carried out to select potential effective wavelengths and these selected wavelengths were further simplified using successive projections algorithm (SPA) for improving the model efficiency. Based on the FMCIA-SPA method for wavelength selection, the optimized models were established using LWPLSR for determination of tuber textural property (TTP) in terms of hardness, resilience, springiness, cohesiveness, gumminess and chewiness, with correlation coefficient of prediction ( R P ) of 0.797, 0.881, 0.584, 0.574, 0.728 and 0.690, respectively. The results of this study demonstrated that FTMIR-ATR spectroscopy could be used reliably and rapidly for the non-destructive assessment of textural property of microwave baked tuber. [ABSTRACT FROM AUTHOR]

The potential of chemical imaging for rapid measurement of dry matter concentration (DMC) and starch concentration (SC) in both potato and sweet potato tubers was investigated. The time series images of tuber samples were acquired, then the resulting reflectance spectra (R S ) were corrected and transformed into absorbance spectra (A S ), and exponent spectra (E S ). Full wavelength regression models including multiple linear regression (MLR), partial least squares regression (PLSR) and locally weighted partial least squares regression (LWPLSR) were established based on spectral profiles with measured DMC and SC values. The best calibration model for measuring DMC and SC was LWPLSR based on E S and R S where the coefficients of determination in cross-validation ( R 2 CV ) were 0.987 and 0.985, and the root mean squared errors in cross-validation (RMSECV) were 0.015 and 0.014, respectively. After, six groups of eight feature wavelengths were chosen from R S , A S and E S based on wavelength selection methods including β-coefficient (βC) of PLSR and the first derivative and mean centering iteration algorithm (FMCIA), and were successively used to build simplified models. The acquired FMCIA-R S -LWPLSR and βC-R S -LWPLSR models showed better accuracy than other simplified models, with R 2 P of 0.985 and RMSEP of 0.016 for DMC prediction, and R 2 P of 0.983 and RMSEP of 0.015 for SC prediction, respectively. Besides, the optimal models for MLR and PLSR were obtained using FMCIA on the basis of the E S . After further reducing the number of feature wavelengths, only six wavelengths (1028, 1068, 1135, 1208, 1262 and 1460 nm) were selected and utilized to develop the simplest FMCIA-Es-MLR model for determining DMC and FMCIA-Es-PLSR model for detecting SC, yielding a reasonable level of accuracy with R 2 P of 0.962 and 0.963 as well as RMSEP of 0.025 and 0.023, respectively. Furthermore, the time series variations of DMC and SC on tuber samples were visualized based on an equation to apply the simplest models to the spectral images. [ABSTRACT FROM AUTHOR]

Two pesticides in terms of chlorpyrifos and imidacloprid contaminating edible jujube fruits were determined using hyperspectral imaging (900–1700 nm) and gas chromatograph-mass spectrometry (GC–MS). Hyperspectral images of jujube samples contaminated by pesticides at different concentrations were collected. Their spectral data extracted in reflectance (R S ), absorbance (A S ), exponent (E S ) and Kubelka–Munck (K-M S ), were respectively used to develop partial least squares discriminant analysis (PLSDA) and locally weighted partial least square regression (LWPLSR) models. Based on these spectral parameters, corresponding models defined as A S -PLSDA and E S -PLSDA acquired optimal results, with correlation coefficients of cross-validation ( R CV ) of more than 0.900 for recognition of chlorpyrifos concentrations and R CV of over 0.713 for identification of concentrations of the imidacloprid. The E S -LWPLSR model obtained the best R CV of 0.864 for quantitative determination of chlorpyrifos residuals, and the best R CV of 0.885 for determination of imidacloprid residuals. The feature wavelengths were selected based on the automatic weighted least squares and gap segment derivative (AWLS-GSD) coupled with regression coefficient (RC) method. The better performance was obtained by the resulting simplified E S -AWLS-GSD-RC-LWPLSR model established using only eight characteristic wavelengths, with R CV of 0.757, RMSECV of 3.75 × 10 −3 for chlorpyrifos residuals, and R CV of 0.898, RMSECV of 0.311 × 10 −3 for imidacloprid residuals. To summarize, hyperspectral imaging technology shows a great potential to predict pesticide residuals of jujube fruit. [ABSTRACT FROM AUTHOR]

When considering food security and huge market interest, a high-efficiency method to ensure the authenticity of the food product is necessary. For this goal, spectral imaging was explored for quantitative detection of Irish organic wheat flour (OWF) adulterated with common wheat flour (WF), cassava flour (CaF) and corn flour (CoF). Hyperspectral images (900–1700 nm) of OWF samples with a series of adulteration percentages were collected. The acquired spectra were pre-processed by second derivative (2nd Der) and standard normal variate (SNV) before modelling. Then partial least squares regression (PLSR) and principal component regression (PCR) were employed for quantitative analysis of adulteration proportion of CoF, CaF and WF in OWF. To develop more effective simplified models, three groups of feature wavelengths were selected from the loading plots of principal component analysis (PCA), and first-derivative and mean centering iteration algorithm (FMCIA). The models developed using FMCIA were better than PCA. After, the corresponding feature wavelengths were further reduced based on model regression coefficients (RC). The optimal result of admixture detection was emerged by the RC-FMCIA-PLSR model, with a determination coefficient of prediction ( R 2 P ) of 0.973 and a root mean square error of prediction (RMSEP) of 0.036 for OWF adulterated with CoF, R 2 P of 0.986 and RMSEP of 0.026 for OWF adulterated with CaF, and R 2 P of 0.971 and RMSEP of 0.038 for OWF adulterated with WF. Visualization maps were generated by calculating the spectral response of each pixel on flour samples. This result indicates that spectral imaging integrated with multivariate analysis has the potential to authenticate the admixtures in specific wheat flour in the range of 3–75% (w/w). [ABSTRACT FROM AUTHOR]

Staple foods, including cereals, legumes, and root/tuber crops, dominate the daily diet of humans by providing valuable proteins, starch, oils, minerals, and vitamins. Quality evaluation of staple foods is primarily carried out on sensory (e.g. external defect, color), adulteration (e.g. species, origin), chemical (e.g. starch, proteins), mycotoxin (e.g.Fusariumtoxin, aflatoxin), parasitic infection (e.g. weevil, beetle), and internal physiological (e.g. hollow heart, black heart) aspects. Conventional methods for the quality assessment of staple foods are always laborious, destructive, and time-consuming. Requirements for online monitoring of staple foods have been proposed to encourage the development of rapid, reagentless, and noninvasive techniques. Spectroscopic techniques, such as visible–infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance spectroscopy, and spectral imaging, have been introduced as promising analytical tools and applied for the quality evaluation of staple foods. This review summarizes the recent applications and progress of such spectroscopic techniques in determining various qualities of staple foods. Besides, challenges and future trends of these spectroscopic techniques are also presented. [ABSTRACT FROM PUBLISHER]

A number of investigations have been conducted to explore non-contacting measuring techniques for predicting chemical components of agricultural product, but there is no research on non-destructive inspection and quantification of water binding capacity (WBC) and specific gravity (SG) in tuber. The purpose of this study was to exploit a rapid analytical technique using reflectance spectra (RS), generalised logarithm spectra (GLS), absorbance spectra (AS), and power spectra (PS) derived from spectral image data to develop partial least squares regression (PLSR) and locally weighted principal component regression (LWPCR) models that predicted tuber WBC and SG. Based upon the RS, GLS, AS, and PS, corresponding feature wavelengths were then respectively selected by using genetic algorithm (GA), first-derivative and mean centering iteration algorithm (FMCIA), and reverse variable algorithm (RVA). Compared to FMCIA and GA, the method of RVA achieved the highest accuracy based on the RVA-PS-LWPCR model for predicting WBC and SG. Then, all combinations of feature wavelengths were refined with the method of regression coefficient (RC). The simplified GA-RC-PS-LWPCR model obtained highest accuracy to measure WBC, resulting in a coefficient of determination in prediction (R2P) of 0.966 and root mean square error of prediction (RMSEP) of 0.199. Besides, the FMCIA-RC-GLS-LWPCR model showed the best performance to determine SG, with R2P of 0.978 and RMSEP of 0.009. The optimal models were then applied to each pixel of the spectral image to generate distribution maps of WBC and SG of tested samples. Furthermore, the overall performances of wavelength selection methods in terms of FMCIA-RC and RVA-RC were equivalent and slightly better than GA-RC. The results demonstrated that effective wavelength selection method can improve the performance of multispectral imaging system for detection of WBC and SG. [ABSTRACT FROM AUTHOR]

This study was conducted to assess the potential feasibility of using hyperspectral imaging (900–1700 nm) for rapid determination of the volatility of tuber compositions (VTC) and prediction of the tuber cooking degree (TCD) in low temperature baking (LTB). Tuber samples of six categories from different origins were imaged and calibrated. The partial least squares regression (PLSR) and three-layer back propagation artificial neural network (TBPANN) models were established to predict VTC and TCD using the entire spectral range and the feature wavelengths. The optimal combination of characteristic wavelengths (991, 1031, 1071, 1138, 1252, 1403, 1460 and 1641 nm) were selected by first derivative and mean centering iteration algorithm (FMCIA) rather than other conventional methods. Based on the qualified eight wavelengths, the FMCIA-TBPANN approach yielded greater overall performance for predicting both VTC and TCD. Furthermore, the distribution maps of VTC and TCD were generated using a resulting function to visualize each pixel of spectral image. This demonstrated the capability of spectral imaging technique for rapid and accurate evaluation of VTC and TCD during LTB. [ABSTRACT FROM AUTHOR]

Based on a new approach for wavelength selection, a multispectral real-time imaging system was proposed for the staple food industry to determine the fidelity of organic spelt flour (OSF) from three categories of adulterants including rye flour (RF), organic wheat flour (OWF) and spelt flour (SF). Calibration models were first built by partial least squares discriminant analysis (PLSDA) and partial least squares regression (PLSR) with spectral pretreatment for multivariate analysis of hyperspectral image in the spectral range of 900–1700 nm. Instead of qualifying certain groups of characteristic wavelengths for RF, OWF, SF and OSF separately, a set of mutual wavelengths (1145, 1192, 1222, 1349, 1359, 1396, 1541, and 1567 nm) was chosen by first-derivative and mean centring iteration algorithm (FMCIA) for all investigated flour samples. Then these selected feature wavelengths were utilized in PLSDA, PLSR and multiple linear regression (MLR) models to devise multispectral imaging system. Better performances for both qualitative discrimination of OSF and quantitative measure of adulterants were emerged in simplified PLSDA and PLSR models, with mean determination coefficients in cross validation ( R 2 CV ) of 0.958 and in prediction ( R 2 P ) of 0.957, respectively. To visualize the adulterants in OSF samples, the distribution maps were drawn by computing the spectral response of each pixel on corresponding spectral images at specific frequencies using a quantitative identification function. The results reveal that spectral imaging integrated with multivariate analysis has good potential for rapidly evaluating the purity of organic spelt flour. [ABSTRACT FROM AUTHOR]

The reliability and veracity of hyperspectral imaging integrated with multivariate analyses were investigated for authentication of sliced organic potato (OP) from non-organic tubers and rapid grading of tubers on the basis of different moisture levels. Hyperspectral images of all the tuber samples were obtained and their spectral data were extracted and pre-processed. Then, partial least squares discriminant analysis (PLSDA) model was established for recognition of the tested samples. Loading plots of the second derivative (SD) and principal component analysis (PCA) were used for selecting characteristic wavelengths. The OP samples were identified correctly (100% accuracy) from non-organic tubers by MC-PLSDA model using only characteristic wavelengths, with predicted sensitivities of 1.000, specificities of ⩾0.944, classification error of ⩽0.028, coefficient of determination ( R 2 P ) of no more than 0.979 and the root mean square error of prediction (RMSEP) of ⩽0.532 for each adulterate type. Another simplified PLSDA model was applied for grading tuber moisture levels, resulting in a correct classification of ⩾91.6%. The visualization results shown on classification maps achieved a rapid and convenient interpretation of tuber varieties and moisture levels. These results indicated that hyperspectral imaging has a great potential for discrimination of OP and identification of tuber moisture levels. [ABSTRACT FROM AUTHOR]

Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382–1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels. [ABSTRACT FROM AUTHOR]

The choice of selecting fruit for canned whole tomatoes is driven by several quality attributes including sweetness, acidity, and softness of tomatoes. Moreover, tomatoes can be contaminated with a variety of molds during cultivation, harvest, and transportation. Conventional evaluation operations for tomato softness and microbial spoilage are usually time-consuming, destructive, and high-cost. One strategy for rapid tomato sorting is to utilize hyper/multispectral imaging. This paper proposes to improve on traditional broad-band infrared imaging of existing color and dirt sorters by increasing the spectral resolution of the information collected. The findings of this study will characterize the potential of the technology in terms of predicting tomato softness and identification of tomato microbial spoilage for further development by the industry. [ABSTRACT FROM AUTHOR]

• DON levels in barley kernels was screened using hyperspectral imaging. • Feature wavelengths were selected and optimized using CARS-ISSPA. • DON contents were predicted using CARS-ISSPA-LWPLSR algorithm. • CARS-ISSPA-PLSDA algorithm classified kernels according to their DON levels. Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382–1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R 2 P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 feature variables yielded R 2 P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25–3 mg/kg, 3–5 mg/kg, and 5–10 mg/kg), with Matthews correlation coefficient in cross-validation (M- R CV) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples. [ABSTRACT FROM AUTHOR]

In many parts of the world, apple trees suffer from severe foliar damage each year due to infection of Alternaria blotch (Alternaria alternata f. sp. Mali), resulting in serious economic losses to growers. Traditional methods for disease detection and severity classification mostly rely on manual labor, which is slow, labor-intensive and highly subjective. There is an urgent need to develop an effective protocol to rapidly and accurately evaluate disease severity. In this study, DeeplabV3+, PSPNet and UNet were used to assess the severity of apple Alternaria leaf blotch. For identifications of leaves and disease areas, the dataset with a total of 5382 samples was randomly split into 74% (4004 samples) for model training, 9% (494 samples) for validation, 8% (444 samples) for testing and 8% (440 samples) for overall testing. Apple leaves were first segmented from complex backgrounds using the deep-learning algorithms with different backbones. Then, the recognition of disease areas was performed on the segmented leaves. The results showed that the PSPNet model with MobileNetV2 backbone exhibited the highest performance in leaf segmentation, with precision, recall and MIoU values of 99.15%, 99.26% and 98.42%, respectively. The UNet model with VGG backbone performed the best in disease-area prediction, with a precision of 95.84%, a recall of 95.54% and a MIoU value of 92.05%. The ratio of disease area to leaf area was calculated to assess the disease severity. The results showed that the average accuracy for severity classification was 96.41%. Moreover, both the correlation coefficient and the consistency correlation coefficient were 0.992, indicating a high agreement between the reference values and the value that the research predicted. This study proves the feasibility of rapid estimation of the severity of apple Alternaria leaf blotch, which will provide technical support for precise application of pesticides. [ABSTRACT FROM AUTHOR]

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars. [ABSTRACT FROM AUTHOR]

Conventional cultivation works to control weeds between the rows, but it ignores the weeds in crop rows which are most competitive with crops. Many vegetable crops still require manual removal of intra-row weeds not otherwise controlled by herbicides or conventional cultivation. The increasing labor costs of weed control and the continued emergences of herbicide-resistant weeds are threatening grower ability to manage weeds and maintain profitability. Intra-row weeders are commercially available but work best in low weed populations. One strategy for rapid weed crop differentiation is to utilize a machine-detectable compound to mark a crop. This paper proposes a new systemic plant signaling technology that can create machine-readable crops to facilitate the automated removal of intra-row weeds in early growth stages. Rhodamine B (Rh–B) is an efficient systemic compound to label crop plants due to its membrane permeability and unique fluorescent properties. The project involves applying solutions of Rh–B at 60 ppm to the roots of lettuce and tomato plants prior to transplantation to evaluate Rh–B persistence in plants under different levels of sunlight. Lettuce and tomato seedlings with the systemic Rh–B should be reliably recognized during their early growth stages. An intelligent robot is expected to be developed to identify the locations of plants based on the systemic signal inside. Reduced light treatments should help to alleviate the photodegradation of Rh–B in plants. After being exposed to full sunlight for 27 days, the systemic Rh–B would be detectable in tomato branches and lettuce ribs, and these plants are tolerant to root treatments with this fluorescent compound. This paper describes the project background and plan as well as the anticipated contributions of the research to allow the machine vision system to reliably identify the crop plants, and thus showing technical feasibility for outdoor weed control. [ABSTRACT FROM AUTHOR]

• A fluorescence macroscopic imaging system using a bandpass filter was constructed. • Rh-B absorbed via the roots from where it moved throughout the celery plant in 24 h. • Photostability of Rh-B and its dose effect on celery vigor were evaluated. • Celery treated with 60 ppm Rh-B for 2 days was photostable for 5 weeks. • The 60 ppm solution of Rh-B was safe for celery plants. Vegetable crops grown in the field are particularly vulnerable to competition from weeds at the seedling stage. To avoid yield loss, weeds must be removed early in the crop cycle. Shortages of farm laborers for hand weeding and lack of effective vegetable herbicides is stimulating the development of intelligent weeders. One challenge for development of effective intelligent weeders is the precision differentiation of vegetable crops and weeds. Crop signaling is a technique that enables rapid and accurate identification of target crops. In this study, an appropriate dose of fluorescent compounds allowed the vegetable crop locations in the field to be reliably detectable by smart machines. The study examined protocols for crop root treatment, computer vision system development, crop signal detection and biomass evaluation. Rhodamine B (Rh-B) is a fluorescent compound with unique optical properties. Different doses of Rh-B were applied to the celery roots for various durations prior to transplantation to assess Rh-B transport in the plant, its photostability and potential impact on seedling growth in the natural outdoor environment of a farm. Systemic Rh-B absorbed via the roots moved throughout the celery plant in 24 h. Compared with 60 ppm solution of Rh-B, higher doses of Rh-B including 90, 180, and 270 ppm resulted in greater absorption by the plant, but such three concentrations injured the plant. The biomass test results showed that the treatment of celery roots for two days with a 60 ppm solution of Rh-B was safe for celery plants. This Rh-B dosage had good photostability in celery seedlings for about 5 weeks after transplanting. An effective dose of systemic Rh-B allowed for the rapid identification of celery plants at early growth stages, thereby facilitating the automatic differentiation of weeds and crops by a robotic machine vision system. [ABSTRACT FROM AUTHOR]

Abstract Nicotinic acetylcholine receptors are cationic channels that mediate fast excitatory transmission in the central nervous system. Several nicotinic acetylcholine receptor subunits have been detected within cerebellar granule cell layer (GCL), and activation of these receptors may have a significant influence on neuronal synaptic transmission of the cerebellum. The aim of present study was to better understand the roles of nicotinic acetylcholine receptors during the sensory stimulation-evoked synaptic transmission in the cerebellar GCL. Our results showed that cerebellar surface perfusion of nicotine significantly facilitated the cerebellar GCL field potential responses evoked by air-puff stimulation of ipsilateral whisker pad, which exhibited increases in amplitude and area under the curve (AUC) of both stimulus onset responses (N1) and stimulus offset responses (N2). The nicotine-induced increase in AUC of facial stimulation-evoked N1 was dose-dependent with a 50% effective concentration (EC 50) of 32.6 μM. Application of either a selective α4β2 nicotinic acetylcholine receptors antagonist, DHβE (1 μM) or a selective α7 nicotinic acetylcholine receptors antagonist, MLA (1 μM) alone attenuated, but not completely abolished the nicotine-induced increases in the amplitude and AUC of the facial stimulation-evoked N1. However, simultaneous blockade of α7 and α4β2 nicotinic acetylcholine receptor subunits abolished the nicotine-induced increase in the amplitude of N1. These results indicate that nicotine activates α7 and α4β2 nicotinic acetylcholine receptor subunits, resulting in an enhancement of facial stimulation-evoked responses in mouse cerebellar GCL. Our results suggest that nicotine modulates the sensory information processing in the cerebellar GCL through α7 and α4β2 subunits nicotinic acetylcholine receptors. [ABSTRACT FROM AUTHOR]

[Display omitted] • The emerging preparation techniques for micro-nano bubbles (MNBs) are reviewed. • The advanced characterization techniques for MNBs are classified. • The burgeoning applications of MNBs in various fields are highlighted. • An assessment of the findings, challenges and knowledge gaps are presented. • Future study directions for MNBs are suggested. Micro-nano bubbles (MNBs) usually refer to ultrafine bubbles less than 100 μm in diameter, exhibiting extraordinary physical and chemical properties compared with those of macro-bubbles, and are attracting increasingly attention in both academia and industry. In the current paper, the full life circle of MNBs was reviewed, from emerging MNBs generation approaches to the advanced characterization techniques, and finally to their recently booming applications. The MNBs generation techniques were classified into two categories: the chemical approaches (e.g., electrolysis and chemical reaction, etc.) and the physical approaches (e.g., cavitation, dispersion of gas, solution mixing, temperature alteration, EHD effect, etc.). As to MNBs characterization, both the static and dynamic characterization techniques were discussed, e.g., optical microscope, high-speed photography, rheology, AFM, DLS, NTA, optical flow cytometry, electron microscope, etc., depending on their operating principles. Merits and demerits of those generation and characterization techniques for MNBs were compared and highlighted, as well as their own characteristics and application scopes. The burgeoning applications of MNBs in functional material preparation were subsequently reviewed in details, including material preparation mechanism, properties and application scenarios. While, the MNBs-based surface cleaning, hydrogen production and carbon neutrality were also emphasized. With the development of basic MNBs theory and the booming scientific and industrial demands, the MNBs-related science and technology will flourish, and MNBs will certainly exhibit broader application prospects in the near future. [ABSTRACT FROM AUTHOR]

[Display omitted] • Foams with long-term stability were generated via little energy input. • Synergy between hydrophilic silica particles and PVP was highlighted for long-term foam stabilization. • PVP adsorption on silica reduces the energy requirement for silica particle adsorption at the air–water interface dramatically. The role of solid particles to act as foam and emulsion stabilizers is well known, providing long-term stability to the two-phase system. However, the ability for particles to generate large volumes of foam with little energy input (low agitation speeds and short agitation times) is difficult to achieve. Poor foamability is due to the slow particle adsorption dynamics and the high adsorption barrier at the air–water interface. In contrast, common surfactants and some surface active polymers which adsorb more readily at the air–water interface exhibit good foamability but often poor foam stability due to the mutual action of multiple destabilization mechanisms. In the current study, we reported a simple bi-component system of hydrophilic silica nanoparticles and poly(vinylpyrrolidone) (PVP) polymer to produce foams with both high foamability and foam stability via simply hand shaking for 1 min. Foams are stabilized by PVP coated silica composite nanoparticles (CPs) with a well-defined core–shell structure. The strong adsorption of PVP onto silica was demonstrated using Quartz Crystal Microbalance with Dissipation monitoring (QCM-D), confirming the formation of an irreversibly and robust adsorbed polymer layer. To better understand the mechanisms for enhanced foamability and foam stability, the interfacial dynamics and the 2-dimensional mechanical properties of the particle-polymer network partitioned at the air–water interface were assessed. The presence of the PVP polymer corona substantially enhanced the adsorption and retention of hydrophilic silica particles at the air/water interface, while the rigid interfacial network formed by the silica nanoparticles guaranteed long-term foam stability (excess of one week), with coarsening fund to be the dominating destabilization mechanism for foam. The enhanced performance is in stark contrast to the individual components, which could only provide good foamability other than foam stability. [ABSTRACT FROM AUTHOR]

Goals: We aimed to evaluate the palliative care quality for terminal cancer patients as a reference for prospective service improvements.Patients and Methods: A prospective study was conducted to analyze the quality audit data collected from 1,476 samples in one medical center's palliative care unit. Lai's modified version of the support team assessment schedule was utilized for 2-year data collection, applying 17 categories of audit criteria rated on a scale of 0 to 4 (0-no symptoms, 4-significant symptoms). The quality audit criteria were scored weekly for 2 weeks following patient admission.Results: Average scores of quality audit criteria from admission to week 2 were 10.05, 8.85, and 8.49, respectively, with statistical differences noted weekly. Efficacy of palliative care was most evident 2 weeks after hospitalization. Factors with statistically significant differences between admission and week 1 included: pain control, constipation, vomiting, anorexia, other symptom control, patient anxiety, self awareness and preparation of patient for illness, self awareness and preparation of family for illness, and satisfaction of patient's spiritual belief and life philosophy. Factors with statistically significant differences between weeks 1 and 2 included: pain control, other symptom control, self awareness and preparation of family for illness, and satisfaction of patient's spiritual belief and life philosophy.Conclusions: Patients in hospice care received adequate symptom relief. Total quality audit scores declined progressively over the 2 weeks following patient admission, indicating efficacy of palliative care quality. [ABSTRACT FROM AUTHOR]

The article provides information on "Be With Patients," a five-day multimodal service learning curriculum for junior medical students at a hospice care center. Details are provided on the lecture series on hospice care and the provision of bedside service. According to the author, service learning can promote civic attitudes, teamwork, and attitudes towards active learning.

Purpose: This study investigated the efficacy of the botanical-derived drug, PG2, a partially purified extract of Astragalus membranaceus, as a complementary and palliative medicine for managing cancer-related fatigue (CRF).Methods: Patients with advanced cancer and moderate to severe CRF were randomized to receive either PG2 or a placebo (normal saline, NS) in the first treatment cycle (four weeks) in a double-blind manner; thereafter, on the next cycle (four weeks), all patients received open-label treatment with PG2.Results: PG2 significantly improved CRF in the NS-primed group. In the first four week cycle, PG2 administration resulted in a greater fatigue-improvement response rate than seen with NS alone. In addition, approximately 82% of patients who reported an improvement of fatigue symptoms following the first cycle of PG2 experienced sustained benefits after administration of the second treatment cycle. Among patients treated with PG2 who did not report an improvement in symptoms throughout the first treatment cycle, approximately 71% showed significant improvement after the second treatment cycle. No major or irreversible toxicities were observed with PG2 treatment.Conclusion: PG2 might be an effective and safe treatment for relieving CRF among advanced cancer patients. [ABSTRACT FROM AUTHOR]