Automated Nondestructive Detection of lnternal lnsect Infestation of Wheat Kernels by Using Near-Infrared Reflectance Spectroscopy

Wheat kernels infested internally with larvae of 3 primary insect pests of grain, the rice weevil, Sitophilus oryzae (L.); the lesser grain borer, Rhyzopertha dominica (F.); and the Angoumois grain moth, Sitotroga cerealella (Olivier), were scanned with a near-infrared spectrometer incorporated into a single kernel characterization system to determine differences in absorption due to the presence of larvae. The single kernel characterization system delivers kernels into the spectrometer viewing area at the rate of 1 per 4s. We were able to differentiate uninfested kernels from kernels infested with larvae of all 3 species by using this automated system. Moisture content, protein content, or wheat class did not affect classification accuracy. The calibration included spectral characteristics in the wavelength ranges of 1,000-1,350 and 1,500-1,680 nm. Larval size was a factor in the sensitivity of the system, with 3rd and 4th instars rice weevil being detected with 95% confidence. In contrast to many other procedures used to detect internal insect infestations in grain, this system could be incorporated into the current grain inspection process and provide the grain industry with quantitative data on internal insect infestations in wheat

Chronological Age-Grading of Three Species of Stored-Product Beetles by Using Near-Infrared Spectroscopy

The accuracy of near-infrared spectroscopy (NIRS) for predicting the chronological age of adults of the rice weevil, Sitophilus oryzae (L.); the lesser grain borer, Rhyzopertha dominica (F.); and the red flour beetle, Tribolium castaneum (Herbst), three pests of stored grain, was examined. NIRS-predicted age correlated well with actual age of these three species. Age predictions in S. oryzae by using the NIRS method are not dependent upon adult sex or temperatures to which adult weevils are exposed. Results indicated that water content decreased with increasing age in rice weevil adults, and excluding wavelengths at which water absorbs NIR radiation reduced the accuracy of correct classification. Additionally, removing cuticular lipids from insects resulted in a significant decrease in classification accuracy of weevils, indicating that these compounds may be partly responsible for the ability of NIRS to differentiate young from old beetles. NIRS is a nondestructive technique that can be used to age-grade large numbers of adult stored-product beetles, information that could help to increase the accuracy of population models for these pest species

Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species.

Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended

Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier

Insect Fragments in Flour: Relationship to Lesser Grain Borer (Coleoptera: Bostrichidae) Infestation Level in Wheat and Rapid Detection Using Near-Infrared Spectroscopy

We determined that the number of insect fragments, quantified using the standard flotation method, in flour milled from wheat infested with larvae, pupae, or preemergent adults of the lesser grain borer, Rhyzopertha dominica (F.), was proportional to infestation level. Wheat infested with a single preemergent adult contributed 28 and 10X as many fragments as wheat infested with a single larva or pupa, respectively. Using regression models that were developed from these data, we predicted that the maximum infestation level that would result in flour with fragment counts below the Food and Drug Administration defect action level (75 fragments/50 g of flour) was 0.95 and 1.5% (380-640 infested kernels/kg of wheat) for pupae and larvae, but it decreased to 0.05% (20 infested kernels/kg) when the grain was infested with preemergent adults. We also reexamined the accuracy and sensitivity of near-infrared spectroscopy (NIRS) for detecting insect fragments in flour by testing three different NIR spectrometers. NIRS-predicted numbers of insect fragments were correlated with the actual number of fragments. NIRS is less precise than the standard flotation method, but it is rapid, nondestructive, does not require extensive sample preparation, and could easily be automated for a more sophisticated sampling protocol for flour based on prescreening samples with NIRS followed up by use of the standard flotation method when necessary

Near-infrared spectroscopy as a complementary age grading and species identification tool for African malaria vectors

Near-infrared spectroscopy (NIRS) was recently applied to age-grade and differentiate laboratory reared Anopheles gambiae sensu strico and Anopheles arabiensis sibling species of Anopheles gambiae sensu lato complex. In this study, we report further on the accuracy of this tool for simultaneously estimating the age class and differentiating the morphologically indistinguishable An. gambiae s.s. and An. arabiensis from semi-field releases and wild populations. Nine different ages (1, 3, 5, 7, 9, 11, 12, 14, 16 d) of An. arabiensis and eight different ages (1, 3, 5, 7, 9, 10, 11, 12 d) of An. gambiae s.s. maintained in 250 × 60 × 40 cm cages within a semi-field large-cage system and 105 wild-caught female An. gambiae s.l., were included in this study. NIRS classified female An. arabiensis and An. gambiae s.s. maintained in semi-field cages as <7 d old or ≥7 d old with 89% (n = 377) and 78% (n = 327) accuracy, respectively, and differentiated them with 89% (n = 704) accuracy. Wild caught An. gambiae s.l. were identified with 90% accuracy (n = 105) whereas their predicted ages were consistent with the expected mean chronological ages of the physiological age categories determined by dissections. These findings have importance for monitoring control programmes where reduction in the proportion of older mosquitoes that have the ability to transmit malaria is an important outcome

Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species.

BACKGROUND: Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. METHODS: Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. RESULTS: The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. CONCLUSION: RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended

Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species

BACKGROUND: Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. METHODS: Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. RESULTS: The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. CONCLUSION: RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended

Using a Near-Infrared Spectrometer to Estimate the Age of Anopheles Mosquitoes Exposed to Pyrethroids

We report on the accuracy of using near-infrared spectroscopy (NIRS) to predict the age of Anopheles mosquitoes reared from wild larvae and a mixed age-wild adult population collected from pit traps after exposure to pyrethroids. The mosquitoes reared from wild larvae were estimated as <7 or ≥7 d old with an overall accuracy of 79%. The age categories of Anopheles mosquitoes that were not exposed to the insecticide papers were predicted with 78% accuracy whereas the age categories of resistant, susceptible and mosquitoes exposed to control papers were predicted with 82%, 78% and 79% accuracy, respectively. The ages of 85% of the wild-collected mixed-age Anopheles were predicted by NIRS as ≤8 d for both susceptible and resistant groups. The age structure of wild-collected mosquitoes was not significantly different for the pyrethroid-susceptible and pyrethroid-resistant mosquitoes (P = 0.210). Based on these findings, NIRS chronological age estimation technique for Anopheles mosquitoes may be independent of insecticide exposure and the environmental conditions to which the mosquitoes are exposed