光谱学与光谱分析 |
|
|
|
|
|
Measurement of Fruit Maturity Based on Laser-Induced Photoluminescence Spectrum |
WANG Le-yan, ZHANG Dong-xian, ZHANG Hai-jun, WANG Xiao-ping |
State Key Laboratory of Modern Optical Instruments, Zhejiang University, Hangzhou 310027, China |
|
|
Abstract Grounding on the concepts of biophotonics measurement, the authors first used a red semiconductor laser (655 nm) to irradiate fruits. Compared with other kinds of illuminating sources, the red semiconductor laser is less expensive and takes little space. The laser-induced photoluminescence spectrums could be detected by coupling fibre-optics probe when the fruits are illuminated by laser. And the spectrum has a distinct peak of relative intensity around the 685 nm wavelength that varies with the degree of fruit maturity. Sugar content measurement was used to prove the laser-induced photoluminescence measurement. The authors tested the sugar content of the fruit specimens, and found that the relative peak value of the fruits’ laser-induced photoluminescence spectrum decreases with the increase in their sugar content. The authors used partial least-squares (PLS) regression to perform an analysis of the relationship between the laser-induced photoluminescence intensity and the sugar content, fitting a curve of the two parameters. The correlation coefficient r of the fitted value and the actual value is 98.92% for red-inside plum and 97.31% for nectarine. So the authors could generalize that there is an approximate linear relationship between the peak value of laser-induced photoluminescence intensity and the sugar content of fruits, and we could use the maturity measurement based on this concept to decide the fruit ripeness. The authors designed the analytic program for this laser-induced photoluminescence spectrum measurement system, which mainly realizes two functions: generating the standard ripe spectrum of a certain kind of fruit from a quantity of their spectra, and, according to this standard spectrum, determining the maturity degree of an unknown spectrum, and at the same time, displaying the unknown laser-induced photoluminescence spectrum. Incorporating this analytic program with the optical spectrometer, it becomes conceivable to test the fruit maturity very conveniently and quickly. The measurement system of fruit maturity based on laser-induced photoluminescence spectrum has also been used to test various fruits. This measurement is nondestructive and inexpensive, and does not require complicated equipment, a feature of great importance in real-time measurement of fruit maturity.
|
Received: 2007-05-28
Accepted: 2007-09-08
|
|
Corresponding Authors:
WANG Le-yan
E-mail: wly@zju.edu.cn
|
|
[1] YE Qi-zheng, YAO Hong-lin, LI Li, et al(叶齐政,姚宏霖,李 黎,等). Plant Physiology Communications(植物生理学通讯), 1999, 35(4): 304. [2] L′homme C, Peschet J L, Puigserver A, et al. Journal of Chromatography A, 2001, 920: 291. [3] Noboru Muramatsu, Naoki Sakurai, Naoki Wada, et al. Postharvest Biology and Technology, 1999, 15: 83. [4] Marc Valente, Jean Yves Ferrandis. Postharvest Biology and Technology, 2003, 29: 219. [5] RIU Yu-kui, HUANG Kun-lun, WANG Wei-min, et al(芮玉奎,黄昆仑,王为民,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(12): 2190. [6] LIU Jie, YU Chang-qing, LI Jia-ze, et al(刘 杰,于常青,李家泽,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2001,21(6): 769. [7] Sarah Schotte, Nele De Bellie, Josse De Baerdemaeker. Postharvest Biology and Technology, 1999, 17: 105. [8] YING Yi-bin, LIU Yan-de, FU Xia-ping(应义斌,刘燕德,傅霞萍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(1): 63. [9] YING Yi-bin, RAO Xiu-qin, MA Jun-fu(应义斌,饶秀勤,马俊福). Transactions of the Chinese Society of Agricultural Engineering(CSAE)(农业工程学报), 2004, 20(2): 144. [10] XU Hui-rong, YING Yi-bin(徐惠荣,应义斌). Journal of Zhejiang University·Agriculture and Life Science(浙江大学学报·农业与生命科学版), 2002, 28(4): 460. [11] Sirinnapa Saranwong, Jinda Sornsrivichai, Sumio Kawano. Postharvest Biology and Technology,2004, 31: 137. [12] HAN Dong-hai, LIU Hai-xin, ZHAO Li-li, et al(韩东海,刘海鑫,赵丽丽,等). Transaction of the Chinese Society of Agricultural Machinery(农业机械学报), 2003, 34(6): 112. [13] Paras N Prasad. Current Opinion in Solid State and Materials Science, 2004, 8: 11. [14] Jennifer Riesz, Joel Gilmore, Paul Meredith. Spectrochimica Acta Part A, 2005, 61: 2153. [15] Polder G, G W A M van der Herjden, et al. Postharvest Biology and Technololgy, 2004, 34: 117. [16] Vogel R, Meredith P, Harvey M D, et al. Spectrochimica Acta Part A, 2004, 60: 245. [17] SONG Yi, ZHANG Dong-xian, LIU Chao(宋 奕,张冬仙,刘 超). Optical Instruments(光学仪器), 2006, 28(3): 17. [18] DUAN Hong-tao, ZHANG Bai, LIU Dian-wei, et al(段洪涛,张 柏,刘殿伟,等). J. Infrared Millim. Waves(红外与毫米波学报), 2006, 25(6): 355. [19] Hyun Kwon Noh, Renfu Lu. Proc of SPIE, 2005, 5996: 599601-1. [20] Ziena H M S. Food Chemistry, 2000, 71: 167. |
[1] |
FAN Ping-ping,LI Xue-ying,QIU Hui-min,HOU Guang-li,LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
[2] |
YANG Chao-pu1, 2, FANG Wen-qing3*, WU Qing-feng3, LI Chun1, LI Xiao-long1. Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 36-43. |
[3] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[4] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[5] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[6] |
LIANG Jin-xing1, 2, 3, XIN Lei1, CHENG Jing-yao1, ZHOU Jing1, LUO Hang1, 3*. Adaptive Weighted Spectral Reconstruction Method Against
Exposure Variation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3330-3338. |
[7] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[8] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[9] |
HUANG Chao1, 2, ZHAO Yu-hong1, ZHANG Hong-ming2*, LÜ Bo2, 3, YIN Xiang-hui1, SHEN Yong-cai4, 5, FU Jia2, LI Jian-kang2, 6. Development and Test of On-Line Spectroscopic System Based on Thermostatic Control Using STM32 Single-Chip Microcomputer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2734-2739. |
[10] |
ZHENG Yi-xuan1, PAN Xiao-xuan2, GUO Hong1*, CHEN Kun-long1, LUO Ao-te-gen3. Application of Spectroscopic Techniques in Investigation of the Mural in Lam Rim Hall of Wudang Lamasery, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2849-2854. |
[11] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
[12] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
[13] |
LUO Dong-jie, WANG Meng, ZHANG Xiao-shuan, XIAO Xin-qing*. Vis/NIR Based Spectral Sensing for SSC of Table Grapes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2146-2152. |
[14] |
WANG Bin1, 2, ZHENG Shao-feng2, GAN Jiu-lin1, LIU Shu3, LI Wei-cai2, YANG Zhong-min1, SONG Wu-yuan4*. Plastic Reference Material (PRM) Combined With Partial Least Square (PLS) in Laser-Induced Breakdown Spectroscopy (LIBS) in the Field of Quantitative Elemental Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2124-2131. |
[15] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
|
|
|
|