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The Jiangmen Underground Neutrino Observatory (JUNO) will install about 18,000 20-inch Photomultiplier Tubes (PMTs) in the center detector to achieve 3%/ √ E(MeV) energy resolution. From the full detector Monte Carlo (MC) simulation, besides the liquid scitillator (LS) and Acrylic nodes, PMT glass has the largest contribution to the natural radioactive background. Various technologies have been developed in the Chinese industry to control the environment and to improve the production process. We have monitored the glass production for more than two months, and the radioactivity in glass was measured using a low background gamma ray spectrometer equipped with a high resolution HPGe detector. The 238 U, 232 Th and 40 K of the glass bulb are reduced by a factor of 2, 9 and 15 respectively, and now they can reach 2.5 Bq/kg for 238 U, 0.5 Bq/kg for 232 Th and 0.5 Bq/kg for 40 K.

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Maturity grading is important for the quality of fruits. Nondestructive maturity detection can be greatly beneficial to the consumer and fruit industry. In this paper, a hyperspectral image of navel oranges was obtained using a diffuse transmittance imaging based system. Multispectral indexes were built to identify the maturity with the hyperspectral technique. Five indexes were proposed to combine the spectra at wavelengths of 640, 760 nm (red edges), and 670 nm (for chlorophyll content) to grade the navel oranges into three maturity stages. The index of ( 670 + 760 − 640 )/( 670 + 760 + 640 ) seemed to be more appropriate to classify maturity, especially to distinguish immature oranges that can be straightly identified in accordance with the value of this index (( 670 + 760 − 640 )/( 670 + 760 + 640 )). Different indexes were used as the input of linear discriminate analysis (LDA) and of -nearest neighbor ( -NN) algorithm to identify the maturity, and it was found that -NN with ( 670 + 760 − 640 )/( 670 + 760 + 640 ) could reach the highest correct classification rate of 96.0%. The results showed that the built index was feasible and accurate in the nondestructive classification of oranges based on the hyperspectral diffuse transmittance imaging. It will greatly help to develop low-cost and real-time multispectral imaging systems for the nondestructive detection of fruit quality in the industry.

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